trigram solution & moved previous implementations
This commit is contained in:
Jakub Adamski
parent
37f40392aa
commit
cf92fd9b73
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@ -1,265 +0,0 @@
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Zadanie 1\n",
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"Wyucz prosty bigramowy model języka oparty na regresji logistycznej (jak przedstawiono na wykładzie)."
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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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"from itertools import islice\n",
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"import regex as re\n",
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"import sys\n",
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"from torchtext.vocab import build_vocab_from_iterator\n",
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"\n",
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"\n",
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"def get_words_from_line(line):\n",
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" line = line.rstrip()\n",
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" yield '<s>'\n",
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" for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n",
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" yield m.group(0).lower()\n",
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" yield '</s>'\n",
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"\n",
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"\n",
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"def get_word_lines_from_file(file_name):\n",
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" with open(file_name, 'r') as fh:\n",
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" for line in fh:\n",
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" yield get_words_from_line(line)\n",
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"\n",
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"vocab_size = 20000\n",
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"\n",
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"vocab = build_vocab_from_iterator(\n",
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" get_word_lines_from_file('test-A/in.tsv'),\n",
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" max_tokens = vocab_size,\n",
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" specials = ['<unk>'])"
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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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{
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"data": {
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"text/plain": [
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"3798"
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]
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},
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"execution_count": 3,
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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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"vocab['welcome']"
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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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{
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"data": {
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"text/plain": [
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"tensor(5.5038e-05, grad_fn=<SelectBackward0>)"
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]
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},
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"execution_count": 10,
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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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"from torch import nn\n",
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"import torch\n",
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"\n",
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"embed_size = 100\n",
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"\n",
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"class SimpleBigramNeuralLanguageModel(nn.Module):\n",
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" def __init__(self, vocabulary_size, embedding_size):\n",
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" super(SimpleBigramNeuralLanguageModel, self).__init__()\n",
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" self.model = nn.Sequential(\n",
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" nn.Embedding(vocabulary_size, embedding_size),\n",
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" nn.Linear(embedding_size, vocabulary_size),\n",
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" nn.Softmax()\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" return self.model(x)\n",
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"\n",
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"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size)\n",
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"\n",
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"vocab.set_default_index(vocab['<unk>'])\n",
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"ixs = torch.tensor(vocab.forward(['welcone']))\n",
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"out = model(ixs)\n",
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"out[0][vocab['to']]"
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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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"from torch.utils.data import IterableDataset\n",
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"import itertools\n",
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"\n",
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"def look_ahead_iterator(gen):\n",
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" prev = None\n",
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" for item in gen:\n",
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" if prev is not None:\n",
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" yield (prev, item)\n",
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" prev = item\n",
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"\n",
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"class Bigrams(IterableDataset):\n",
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" def __init__(self, text_file, vocabulary_size):\n",
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" self.vocab = build_vocab_from_iterator(\n",
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" get_word_lines_from_file(text_file),\n",
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" max_tokens = vocabulary_size,\n",
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" specials = ['<unk>'])\n",
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" self.vocab.set_default_index(self.vocab['<unk>'])\n",
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" self.vocabulary_size = vocabulary_size\n",
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" self.text_file = text_file\n",
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"\n",
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" def __iter__(self):\n",
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" return look_ahead_iterator(\n",
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" (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))\n",
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"\n",
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"train_dataset = Bigrams('test-A/in.tsv', vocab_size)"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0 tensor(10.0928, grad_fn=<NllLossBackward0>)\n",
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"100 tensor(8.4572, grad_fn=<NllLossBackward0>)\n",
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"200 tensor(7.6165, grad_fn=<NllLossBackward0>)\n",
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"300 tensor(6.9356, grad_fn=<NllLossBackward0>)\n",
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"400 tensor(6.5687, grad_fn=<NllLossBackward0>)\n",
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"500 tensor(6.2197, grad_fn=<NllLossBackward0>)\n"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[13], line 15\u001b[0m\n\u001b[0;32m 13\u001b[0m y \u001b[39m=\u001b[39m y\u001b[39m.\u001b[39mto(device)\n\u001b[0;32m 14\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n\u001b[1;32m---> 15\u001b[0m ypredicted \u001b[39m=\u001b[39m model(x)\n\u001b[0;32m 16\u001b[0m loss \u001b[39m=\u001b[39m criterion(torch\u001b[39m.\u001b[39mlog(ypredicted), y)\n\u001b[0;32m 17\u001b[0m \u001b[39mif\u001b[39;00m step \u001b[39m%\u001b[39m \u001b[39m100\u001b[39m \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n",
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"File \u001b[1;32mc:\\Users\\jadamski\\.conda\\envs\\modelowanie\\lib\\site-packages\\torch\\nn\\modules\\module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
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"Cell \u001b[1;32mIn[10], line 16\u001b[0m, in \u001b[0;36mSimpleBigramNeuralLanguageModel.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[1;32m---> 16\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel(x)\n",
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"File \u001b[1;32mc:\\Users\\jadamski\\.conda\\envs\\modelowanie\\lib\\site-packages\\torch\\nn\\modules\\module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
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"File \u001b[1;32mc:\\Users\\jadamski\\.conda\\envs\\modelowanie\\lib\\site-packages\\torch\\nn\\modules\\container.py:217\u001b[0m, in \u001b[0;36mSequential.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 215\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m):\n\u001b[0;32m 216\u001b[0m \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m:\n\u001b[1;32m--> 217\u001b[0m \u001b[39minput\u001b[39m \u001b[39m=\u001b[39m module(\u001b[39minput\u001b[39;49m)\n\u001b[0;32m 218\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39minput\u001b[39m\n",
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"File \u001b[1;32mc:\\Users\\jadamski\\.conda\\envs\\modelowanie\\lib\\site-packages\\torch\\nn\\modules\\module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
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"File \u001b[1;32mc:\\Users\\jadamski\\.conda\\envs\\modelowanie\\lib\\site-packages\\torch\\nn\\modules\\linear.py:114\u001b[0m, in \u001b[0;36mLinear.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 113\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m--> 114\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mlinear(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
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]
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}
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],
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"source": [
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"from torch.utils.data import DataLoader\n",
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"\n",
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"device = 'cpu' # cuda\n",
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"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
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"data = DataLoader(train_dataset, batch_size=5000)\n",
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"optimizer = torch.optim.Adam(model.parameters())\n",
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"criterion = torch.nn.NLLLoss()\n",
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"\n",
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"model.train()\n",
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"step = 0\n",
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"for x, y in data:\n",
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" x = x.to(device)\n",
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" y = y.to(device)\n",
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" optimizer.zero_grad()\n",
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" ypredicted = model(x)\n",
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" loss = criterion(torch.log(ypredicted), y)\n",
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" if step % 100 == 0:\n",
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" print(step, loss)\n",
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" step += 1\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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"\n",
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"torch.save(model.state_dict(), 'model1.bin')"
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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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{
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"data": {
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"text/plain": [
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"[('liquid', 6933, 0.0004737793351523578),\n",
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" ('bia', 5842, 0.00043268679291941226),\n",
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" ('sole', 6386, 0.0004295798426028341),\n",
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" ('nmeant', 17711, 0.00034942160709761083),\n",
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" ('savs', 16709, 0.00034736539237201214),\n",
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" ('striving', 12414, 0.0003441996523179114),\n",
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" ('nol', 2640, 0.00032789510441944003),\n",
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" ('imposing', 8457, 0.0003199590719304979),\n",
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" ('hound', 17348, 0.00031824613688513637),\n",
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" ('?\"\\\\', 4294, 0.0003141215711366385)]"
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]
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},
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"execution_count": 16,
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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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"device = 'cpu' # cuda\n",
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"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
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"#model.load_state_dict(torch.load('model1.bin'))\n",
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"model.eval()\n",
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"\n",
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"ixs = torch.tensor(vocab.forward(['welcome'])).to(device)\n",
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"\n",
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"out = model(ixs)\n",
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"top = torch.topk(out[0], 10)\n",
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"top_indices = top.indices.tolist()\n",
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"top_probs = top.values.tolist()\n",
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"top_words = vocab.lookup_tokens(top_indices)\n",
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"list(zip(top_words, top_indices, top_probs))"
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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": "modelowanie",
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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",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.10"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
80
bigram.py
80
bigram.py
|
|
@ -1,80 +0,0 @@
|
|||
import collections
|
||||
import re
|
||||
import random
|
||||
import math
|
||||
|
||||
input_file_path = "train/in.tsv"
|
||||
bigrams = collections.defaultdict(lambda: collections.defaultdict(int))
|
||||
|
||||
|
||||
def clean_text(text: str):
|
||||
text = text.replace('\n', ' ')
|
||||
text = re.sub(r'[^a-zA-Z0-9\s]', '', text)
|
||||
text = text.lower()
|
||||
text = text.strip()
|
||||
return text
|
||||
|
||||
|
||||
with open('train/expected.tsv', 'r', encoding="utf-8") as f:
|
||||
expected = [line for line in f]
|
||||
|
||||
with open(input_file_path, 'r', encoding="utf-8") as f:
|
||||
data = [line.split('\t') for line in f]
|
||||
|
||||
#data = data[:200000] # total is over 400 000
|
||||
|
||||
combined = []
|
||||
|
||||
for idx, row in enumerate(data):
|
||||
line = clean_text(row[6]) + ' ' + expected[idx] + ' ' + clean_text(row[7])
|
||||
combined.append(line.lower())
|
||||
|
||||
|
||||
for line in combined:
|
||||
tokens = re.findall(r"\b\w+\b", line)
|
||||
|
||||
for i in range(len(tokens) - 1):
|
||||
bigrams[tokens[i]][tokens[i+1]] += 1
|
||||
|
||||
|
||||
|
||||
most_popular_words = [
|
||||
"be:0.5 and:0.2 of:0.1 :0.2",
|
||||
"a:0.5 in:0.2 to:0.1 :0.2",
|
||||
"have:0.5 too:0.2 it:0.1 :0.2",
|
||||
"I:0.5 that:0.2 for:0.1 :0.2",
|
||||
"you:0.5 he:0.2 with:0.1 :0.2",
|
||||
"on:0.5 do:0.2 say:0.1 :0.2",
|
||||
"this:0.5 they:0.2 at:0.1 :0.2",
|
||||
"but:0.5 we:0.2 his:0.1 :0.2"
|
||||
]
|
||||
|
||||
|
||||
with open('test-A/in.tsv', "r", encoding="utf-8") as input_file, open('test-A/out.tsv', "w", encoding="utf-8") as output_file:
|
||||
|
||||
lines = input_file.readlines()
|
||||
|
||||
for idx, line in enumerate(lines):
|
||||
tokens = re.findall(r"\b\w+\b", clean_text(line.split("\t")[6]))
|
||||
|
||||
probabilities = []
|
||||
denominator = sum(bigrams[tokens[-1]].values())
|
||||
|
||||
for possible_word in bigrams[tokens[-1]]:
|
||||
probability = bigrams[tokens[-1]][possible_word] / denominator
|
||||
probabilities.append((possible_word, probability))
|
||||
|
||||
probabilities.sort(key=lambda x: x[1], reverse=True)
|
||||
print(f'Line {idx} of {len(lines)}')
|
||||
|
||||
if len(probabilities) >= 3:
|
||||
out_line = ""
|
||||
out_line += probabilities[0][0] + ":0.6 "
|
||||
out_line += probabilities[1][0] + ":0.2 "
|
||||
out_line += probabilities[2][0] + ":0.1 "
|
||||
out_line += ":0.1"
|
||||
output_file.write(out_line + "\n")
|
||||
|
||||
else:
|
||||
output_file.write(random.choice(most_popular_words) + "\n")
|
||||
|
||||
20898
dev-0/out.tsv
20898
dev-0/out.tsv
File diff suppressed because it is too large
Load Diff
24
simple.py
24
simple.py
|
|
@ -1,24 +0,0 @@
|
|||
import random
|
||||
|
||||
most_popular_words = [
|
||||
"be:0.5 and:0.2 of:0.1 :0.2",
|
||||
"a:0.5 in:0.2 to:0.1 :0.2",
|
||||
"have:0.5 too:0.2 it:0.1 :0.2",
|
||||
"I:0.5 that:0.2 for:0.1 :0.2",
|
||||
"you:0.5 he:0.2 with:0.1 :0.2",
|
||||
"on:0.5 do:0.2 say:0.1 :0.2",
|
||||
"this:0.5 they:0.2 at:0.1 :0.2",
|
||||
"but:0.5 we:0.2 his:0.1 :0.2"
|
||||
]
|
||||
|
||||
folder = "dev-0"
|
||||
|
||||
with open(folder + "/in.tsv", "r", encoding='utf-8') as in_file:
|
||||
lines = in_file.readlines()
|
||||
with open(folder + "/out.tsv", "w", encoding='utf-8') as out_file:
|
||||
for line in lines:
|
||||
out_file.write(random.choice(most_popular_words) + "\n")
|
||||
|
||||
|
||||
# słowo:prawdopodobieństwo słowo:prawdopodobieństwo :prawdopodobieństwo-reszty słów
|
||||
# "the:0.2 at:0.3 :0.1"
|
||||
14586
test-A/out.tsv
14586
test-A/out.tsv
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue