solving the task
This commit is contained in:
Rafał Sobański
parent
756ef4277a
commit
ad803e1b81
144
.ipynb_checkpoints/Logistic_Regression-checkpoint.ipynb
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144
.ipynb_checkpoints/Logistic_Regression-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": 43,
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"id": "f607767c",
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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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"import gensim.downloader as gensim\n",
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"from nltk.tokenize import word_tokenize"
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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": 35,
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"id": "58212f9c",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = pd.read_table('train/in.tsv', sep='\\t', header = None)\n",
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"y_train = pd.read_table('train/expected.tsv', sep='\\t', header = None)\n",
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"y_train = y_train[0]\n",
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"x_dev = pd.read_table('dev-0/in.tsv', sep='\\t', header = None)\n",
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"x_test = pd.read_table('test-A/in.tsv', sep='\\t', header = None)\n",
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"\n",
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"x_train = x_train[0].str.lower()\n",
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"x_dev = x_dev[0].str.lower()\n",
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"x_test = x_test[0].str.lower()\n",
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"\n",
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"x_train = [word_tokenize(x) for x in x_train]\n",
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"x_dev = [word_tokenize(x) for x in x_dev]\n",
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"x_test = [word_tokenize(x) for x 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": 56,
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"id": "d2ef6f47",
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"metadata": {},
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"outputs": [],
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"source": [
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"word2vec = gensim.load('glove-wiki-gigaword-50')\n",
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"\n",
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"def document_vector(doc):\n",
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" return np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(50)], axis=0)\n",
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"\n",
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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": 76,
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"id": "c8f52012",
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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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" def __init__(self, features):\n",
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" super(NeuralNetworkModel, self).__init__()\n",
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" self.fc1 = torch.nn.Linear(features, 50)\n",
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" self.fc2 = torch.nn.Linear(50, 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\n",
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" \n",
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"nn_model = NeuralNetworkModel(100)\n",
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"BATCH_SIZE = 5\n",
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"criterion = torch.nn.BCELoss()\n",
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"optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)"
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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": 77,
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"id": "47c18496",
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"metadata": {},
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"outputs": [
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{
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"ename": "RuntimeError",
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"evalue": "mat1 and mat2 shapes cannot be multiplied (5x50 and 100x50)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-77-09c84be1af2b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 888\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 890\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 891\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m<ipython-input-76-4843ffd45fe7>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 888\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 890\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 891\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(input, weight, bias)\u001b[0m\n\u001b[1;32m 1751\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhas_torch_function_variadic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1752\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mhandle_torch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1753\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1754\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (5x50 and 100x50)"
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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(5):\n",
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" nn_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 = nn_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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"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",
|
||||
"version": "3.9.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
94
Logistic-Regression.py
Normal file
94
Logistic-Regression.py
Normal file
@ -0,0 +1,94 @@
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import torch
|
||||
import gensim.downloader as gensim
|
||||
from nltk.tokenize import word_tokenize
|
||||
|
||||
x_train = pd.read_table('train/in.tsv', sep='\t', header = None, error_bad_lines = False, quoting = 3)
|
||||
y_train = pd.read_table('train/expected.tsv', sep='\t', header = None, quoting = 3)
|
||||
y_train = y_train[0]
|
||||
x_dev = pd.read_table('dev-0/in.tsv', sep='\t', header = None, quoting = 3)
|
||||
x_test = pd.read_table('test-A/in.tsv', sep='\t', header = None, quoting = 3)
|
||||
|
||||
x_train = x_train[0].str.lower()
|
||||
x_dev = x_dev[0].str.lower()
|
||||
x_test = x_test[0].str.lower()
|
||||
|
||||
x_train = [word_tokenize(x) for x in x_train]
|
||||
x_dev = [word_tokenize(x) for x in x_dev]
|
||||
x_test = [word_tokenize(x) for x in x_test]
|
||||
|
||||
word2vec = gensim.load('glove-wiki-gigaword-50')
|
||||
|
||||
def document_vector(doc):
|
||||
return np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(50)], 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 NeuralNetworkModel(torch.nn.Module):
|
||||
def __init__(self, features):
|
||||
super(NeuralNetworkModel, self).__init__()
|
||||
self.fc1 = torch.nn.Linear(50, features)
|
||||
self.fc2 = torch.nn.Linear(features, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = torch.relu(x)
|
||||
x = self.fc2(x)
|
||||
x = torch.sigmoid(x)
|
||||
return x
|
||||
|
||||
nn_model = NeuralNetworkModel(100)
|
||||
BATCH_SIZE = 5
|
||||
criterion = torch.nn.BCELoss()
|
||||
optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)
|
||||
|
||||
for epoch in range(5):
|
||||
nn_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 = nn_model(X.float())
|
||||
loss = criterion(outputs, y)
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
y_dev = []
|
||||
y_test = []
|
||||
|
||||
nn_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 = nn_model(X.float())
|
||||
|
||||
y = (outputs > 0.5)
|
||||
y_dev.extend(y)
|
||||
|
||||
for i in range(0, len(x_test), BATCH_SIZE):
|
||||
X = x_test[i:i+BATCH_SIZE]
|
||||
X = torch.tensor(X)
|
||||
|
||||
outputs = nn_model(X.float())
|
||||
|
||||
y = (outputs > 0.5)
|
||||
y_test.extend(y)
|
||||
|
||||
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)
|
||||
5272
dev-0/out.tsv
Normal file
5272
dev-0/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
5
geval-results.txt
Normal file
5
geval-results.txt
Normal file
@ -0,0 +1,5 @@
|
||||
Likelihood 0.0000
|
||||
Accuracy 0.7289
|
||||
F1.0 0.5594
|
||||
Precision 0.6587
|
||||
Recall 0.4861
|
||||
5152
test-A/out.tsv
Normal file
5152
test-A/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user