logistic regression
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.ipynb_checkpoints/LogisticRegression-checkpoint.ipynb
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269
.ipynb_checkpoints/LogisticRegression-checkpoint.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import torch\n",
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"from nltk.tokenize import word_tokenize\n",
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"import gensim.downloader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"#wczytywanie danych\n",
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"x_train = pd.read_table('train/in.tsv', sep='\\t', error_bad_lines=False, quoting=3, header=None, names=['content', 'id'], usecols=['content'])\n",
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"y_train = pd.read_table('train/expected.tsv', sep='\\t', error_bad_lines=False, quoting=3, header=None, names=['label'])\n",
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"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",
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"x_test = pd.read_table('test-A/in.tsv', sep='\\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = x_train.content.str.lower()\n",
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"x_dev = x_dev.content.str.lower()\n",
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"x_test = x_test.content.str.lower()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[nltk_data] Downloading package punkt to /home/tomasz/nltk_data...\n",
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"[nltk_data] Unzipping tokenizers/punkt.zip.\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import nltk\n",
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"nltk.download('punkt')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = [word_tokenize(content) for content in x_train]\n",
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"x_dev = [word_tokenize(content) for content in x_dev]\n",
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"x_test = [word_tokenize(content) for content in x_test]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"word2vec = gensim.downloader.load(\"word2vec-google-news-300\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def document_vector(doc):\n",
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" \"\"\"Create document vectors by averaging word vectors. Remove out-of-vocabulary words.\"\"\"\n",
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" return np.mean([word2vec[w] for w in doc if w in word2vec] or [np.zeros(300)], axis=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = [document_vector(doc) for doc in x_train]\n",
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"x_dev = [document_vector(doc) for doc in x_dev]\n",
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"x_test = [document_vector(doc) for doc in x_test]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"class NeuralNetwork(torch.nn.Module): \n",
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" def __init__(self, hidden_size):\n",
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" super(NeuralNetwork, self).__init__()\n",
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" self.l1 = torch.nn.Linear(300, hidden_size)\n",
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" self.l2 = torch.nn.Linear(hidden_size, 1)\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.l1(x)\n",
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" x = torch.relu(x)\n",
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" x = self.l2(x)\n",
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" x = torch.sigmoid(x)\n",
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" return x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"hidden_size = 600\n",
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"epochs = 5\n",
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"batch_size = 15\n",
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"model = NeuralNetwork(hidden_size)\n",
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"criterion = torch.nn.BCELoss()\n",
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"optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/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",
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" Variable._execution_engine.run_backward(\n"
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]
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}
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],
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"source": [
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"for epoch in range(epochs):\n",
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" model.train()\n",
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" for i in range(0, y_train.shape[0], batch_size):\n",
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" X = x_train[i:i+batch_size]\n",
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" X = torch.tensor(X)\n",
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" y = y_train[i:i+batch_size]\n",
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" y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)\n",
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" \n",
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" outputs = model(X.float())\n",
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" loss = criterion(outputs, y)\n",
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" \n",
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" optimizer.zero_grad()\n",
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" loss.backward()\n",
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" optimizer.step()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_dev = []\n",
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"y_test = []"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"NeuralNetwork(\n",
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" (l1): Linear(in_features=300, out_features=600, bias=True)\n",
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" (l2): Linear(in_features=600, out_features=1, bias=True)\n",
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")"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model.eval()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"with torch.no_grad():\n",
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" for i in range(0, len(x_dev), batch_size):\n",
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" X = x_dev[i:i+batch_size]\n",
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" X = torch.tensor(X)\n",
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" outputs = model(X.float()) \n",
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" prediction = (outputs > 0.5)\n",
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" y_dev += prediction.tolist()\n",
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"\n",
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" for i in range(0, len(x_test), batch_size):\n",
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" X = x_test[i:i+batch_size]\n",
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" X = torch.tensor(X)\n",
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" outputs = model(X.float())\n",
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" y = (outputs > 0.5)\n",
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" y_test += prediction.tolist()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_dev = np.asarray(y_dev, dtype=np.int32)\n",
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"y_test = np.asarray(y_test, dtype=np.int32)\n",
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"\n",
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"y_dev = pd.DataFrame({'label':y_dev})\n",
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"y_test = pd.DataFrame({'label':y_test})\n",
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"\n",
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"y_dev.to_csv(r'dev-0/out.tsv', sep='\\t', index=False, header=False)\n",
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"y_test.to_csv(r'test-A/out.tsv', sep='\\t', index=False, header=False)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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95
Log_Reg.py
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95
Log_Reg.py
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import pandas as pd
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import numpy as np
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import torch
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from nltk.tokenize import word_tokenize
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import gensim.downloader
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x_train = pd.read_table('train/in.tsv', sep='\t', error_bad_lines=False, quoting=3, header=None, names=['content', 'id'], usecols=['content'])
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y_train = pd.read_table('train/expected.tsv', sep='\t', error_bad_lines=False, quoting=3, header=None, names=['label'])
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x_dev = pd.read_table('dev-0/in.tsv', sep='\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])
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x_test = pd.read_table('test-A/in.tsv', sep='\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])
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x_train = x_train.content.str.lower()
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x_dev = x_dev.content.str.lower()
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x_test = x_test.content.str.lower()
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x_train = [word_tokenize(content) for content in x_train]
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x_dev = [word_tokenize(content) for content in x_dev]
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x_test = [word_tokenize(content) for content in x_test]
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word2vec = gensim.downloader.load("word2vec-google-news-300")
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def document_vector(doc):
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"""Create document vectors by averaging word vectors. Remove out-of-vocabulary words."""
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return np.mean([word2vec[w] for w in doc if w in word2vec] or [np.zeros(300)], axis=0)
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x_train = [document_vector(doc) for doc in x_train]
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x_dev = [document_vector(doc) for doc in x_dev]
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x_test = [document_vector(doc) for doc in x_test]
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class NeuralNetwork(torch.nn.Module):
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def __init__(self, hidden_size):
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super(NeuralNetwork, self).__init__()
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self.l1 = torch.nn.Linear(300, hidden_size)
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self.l2 = torch.nn.Linear(hidden_size, 1)
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def forward(self, x):
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x = self.l1(x)
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x = torch.relu(x)
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x = self.l2(x)
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x = torch.sigmoid(x)
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return x
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hidden_size = 600
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epochs = 5
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batch_size = 15
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model = NeuralNetwork(hidden_size)
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criterion = torch.nn.BCELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
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for epoch in range(epochs):
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model.train()
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for i in range(0, y_train.shape[0], batch_size):
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X = x_train[i:i+batch_size]
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X = torch.tensor(X)
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y = y_train[i:i+batch_size]
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y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
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outputs = model(X.float())
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loss = criterion(outputs, y)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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y_dev = []
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y_test = []
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model.eval()
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with torch.no_grad():
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for i in range(0, len(x_dev), batch_size):
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X = x_dev[i:i+batch_size]
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X = torch.tensor(X)
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outputs = model(X.float())
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prediction = (outputs > 0.5)
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y_dev.extend(prediction)
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for i in range(0, len(x_test), batch_size):
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X = x_test[i:i+batch_size]
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X = torch.tensor(X)
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outputs = model(X.float())
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y = (outputs > 0.5)
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y_test.extend(prediction)
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y_dev = np.asarray(y_dev, dtype=np.int32)
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y_test = np.asarray(y_test, dtype=np.int32)
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y_dev = pd.DataFrame({'label':y_dev})
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y_test = pd.DataFrame({'label':y_test})
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y_dev.to_csv(r'dev-0/out.tsv', sep='\t', index=False, header=False)
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y_test.to_csv(r'test-A/out.tsv', sep='\t', index=False, header=False)
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269
LogisticRegression.ipynb
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269
LogisticRegression.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import torch\n",
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"from nltk.tokenize import word_tokenize\n",
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"import gensim.downloader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"#wczytywanie danych\n",
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"x_train = pd.read_table('train/in.tsv', sep='\\t', error_bad_lines=False, quoting=3, header=None, names=['content', 'id'], usecols=['content'])\n",
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"y_train = pd.read_table('train/expected.tsv', sep='\\t', error_bad_lines=False, quoting=3, header=None, names=['label'])\n",
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"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",
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"x_test = pd.read_table('test-A/in.tsv', sep='\\t', error_bad_lines=False, header=None, quoting=3, names=['content', 'id'], usecols=['content'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = x_train.content.str.lower()\n",
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"x_dev = x_dev.content.str.lower()\n",
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"x_test = x_test.content.str.lower()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[nltk_data] Downloading package punkt to /home/tomasz/nltk_data...\n",
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"[nltk_data] Unzipping tokenizers/punkt.zip.\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import nltk\n",
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"nltk.download('punkt')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = [word_tokenize(content) for content in x_train]\n",
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"x_dev = [word_tokenize(content) for content in x_dev]\n",
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"x_test = [word_tokenize(content) for content in x_test]"
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]
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},
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{
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"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": "3.8.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
2398
dev-0/out.tsv
2398
dev-0/out.tsv
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Load Diff
2744
test-A/out.tsv
2744
test-A/out.tsv
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Load Diff
Loading…
Reference in New Issue
Block a user