461 lines
12 KiB
Plaintext
461 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import gensim\n",
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"import torch\n",
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"import pandas as pd\n",
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"from gensim.models import Word2Vec\n",
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"from gensim import downloader\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"BATCH_SIZE = 10\n",
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"EPOCHS = 100\n",
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"FEAUTERES = 200\n"
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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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"class NeuralNetworkModel(torch.nn.Module):\n",
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" \n",
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" def __init__(self):\n",
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" super(NeuralNetworkModel, self).__init__()\n",
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" self.fc1 = torch.nn.Linear(FEAUTERES,500)\n",
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" self.fc2 = torch.nn.Linear(500,1)\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.fc1(x)\n",
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" x = torch.relu(x)\n",
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" x = self.fc2(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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"word2vec = downloader.load(\"glove-twitter-200\")"
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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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"def readData(fileName): \n",
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" with open(f'{fileName}/in.tsv', 'r', encoding='utf8') as f:\n",
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" X = np.array([x.strip().lower() for x in f.readlines()])\n",
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" with open(f'{fileName}/expected.tsv', 'r', encoding='utf8') as f:\n",
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" y = np.array([int(x.strip()) for x in f.readlines()])\n",
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" return X,y"
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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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"X_file,y_file = readData('dev-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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train_w2v = [np.mean([word2vec[word.lower()] for word in doc.split() if word.lower() in word2vec]\n",
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" or [np.zeros(FEAUTERES)], axis=0) for doc in X_file]"
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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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"def train_model(X_file,y_file):\n",
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" model = NeuralNetworkModel()\n",
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"\n",
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" criterion = torch.nn.BCELoss()\n",
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" optimizer = torch.optim.ASGD(model.parameters(), lr=0.05)\n",
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" for epoch in range(EPOCHS):\n",
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" print(epoch)\n",
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" loss_score = 0\n",
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" acc_score = 0\n",
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" items_total = 0\n",
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" for i in range(0, y_file.shape[0], BATCH_SIZE):\n",
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" x = X_file[i:i+BATCH_SIZE]\n",
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" x = torch.tensor(np.array(x).astype(np.float32))\n",
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" y = y_file[i:i+BATCH_SIZE]\n",
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" y = torch.tensor(y.astype(np.float32)).reshape(-1, 1)\n",
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" y_pred = model(x)\n",
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" acc_score += torch.sum((y_pred > 0.5) == y).item()\n",
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" items_total += y.shape[0]\n",
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"\n",
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" optimizer.zero_grad()\n",
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" loss = criterion(y_pred, y)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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"\n",
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" loss_score += loss.item() * y.shape[0]\n",
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" \n",
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" print((loss_score / items_total), (acc_score / items_total))\n",
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" return model"
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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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"def predict(model,x_file):\n",
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" y_dev = []\n",
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" with torch.no_grad():\n",
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" for i in range(0, len(x_file), BATCH_SIZE):\n",
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" x = x_file[i:i+BATCH_SIZE]\n",
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" x = torch.tensor(np.array(x).astype(np.float32))\n",
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" outputs = model(x)\n",
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" y = (outputs > 0.5)\n",
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" y_dev.extend(y)\n",
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" return y_dev\n",
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" "
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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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"def wrtieToFile(fileName,y_file):\n",
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" y_out = []\n",
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" for y in y_file:\n",
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" y_out.append(int(str(y[0]).split('(')[1].split(')')[0]=='True'))\n",
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" with open(f'{fileName}/out.tsv','w',encoding='utf8') as f:\n",
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" for y in y_out:\n",
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" f.write(f'{y}\\n')"
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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": "stdout",
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"output_type": "stream",
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}
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],
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"source": [
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"model = train_model(x_train_w2v,y_file)"
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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=predict(model,x_train_w2v)"
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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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"source": [
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"wrtieToFile(\"dev-0\",y_dev)"
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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 open(f'test-A/in.tsv', 'r', encoding='utf8') as f:\n",
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" X = np.array([x.strip().lower() for x in f.readlines()])"
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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": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train_w2v = [np.mean([word2vec[word.lower()] for word in doc.split() if word.lower() in word2vec]\n",
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" or [np.zeros(FEAUTERES)], axis=0) for doc in 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": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_dev=predict(model,x_train_w2v)"
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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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"wrtieToFile(\"test-A\",y_dev)"
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]
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}
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],
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"metadata": {
|
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"interpreter": {
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"hash": "f08154012ddadd8e950e6e9e035c7a7b32c136e7647e9b7c77e02eb723a8bedb"
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},
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"kernelspec": {
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"display_name": "Python 3.9.7 ('base')",
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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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"name": "ipython",
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"version": 3
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},
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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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},
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}
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