paranormal-or-skeptic-ISI-s.../LogReg_Test.ipynb
Dominik Strzako 68a99a2c2d Test Outputs
2021-05-22 15:05:39 +02:00

252 lines
6.6 KiB
Plaintext

{
"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
}