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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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"from torchtext.vocab import build_vocab_from_iterator\n",
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"from torch.utils.data import DataLoader\n",
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"import torch\n",
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"from torch import nn\n",
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"import pandas as pd\n",
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"import nltk\n",
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"import regex as re\n",
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"import csv\n",
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"import itertools\n",
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"from nltk import word_tokenize\n",
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"from os.path import exists\n",
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"\n",
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"\n",
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"def clean(text):\n",
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" text = str(text).strip().lower()\n",
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" text = re.sub(\"’|>|<|\\.|\\\\|\\\"|”|-|,|\\*|:|\\/\", \"\", text)\n",
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" text = text.replace('\\\\n', \" \").replace(\"'t\", \" not\").replace(\"'s\", \" is\").replace(\"'ll\", \" will\").replace(\"'m\", \" am\").replace(\"'ve\", \" have\")\n",
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" text = text.replace(\"'\", \"\")\n",
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" return text\n",
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"\n",
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"def get_words_from_line(line, specials = True):\n",
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" line = line.rstrip()\n",
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" if specials:\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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" if specials:\n",
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" yield '</s>'\n",
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"\n",
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"def get_word_lines_from_data(d):\n",
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" for line in d:\n",
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" yield get_words_from_line(line)\n",
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"\n",
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"\n",
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"class SimpleBigramNeuralLanguageModel(torch.nn.Module):\n",
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" \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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"def look_ahead_iterator(gen):\n",
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" w1 = None\n",
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" for item in gen:\n",
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" if w1 is not None:\n",
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" yield (w1, item)\n",
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" w1 = item\n",
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" \n",
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"class Bigrams(torch.utils.data.IterableDataset):\n",
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" def __init__(self, data, vocabulary_size):\n",
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" self.vocab = build_vocab_from_iterator(\n",
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" get_word_lines_from_data(data),\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.data = data\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_data(self.data))))\n",
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"\n",
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"\n",
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"# ładowanie danych treningowych\n",
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"in_file = 'train/in.tsv.xz'\n",
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"out_file = 'train/expected.tsv'\n",
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"\n",
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"X_train = pd.read_csv(in_file, sep='\\t', header=None, quoting=csv.QUOTE_NONE, nrows=200000, on_bad_lines=\"skip\", encoding=\"UTF-8\")\n",
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"Y_train = pd.read_csv(out_file, sep='\\t', header=None, quoting=csv.QUOTE_NONE, nrows=200000, on_bad_lines=\"skip\", encoding=\"UTF-8\")\n",
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"\n",
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"X_train = X_train[[6, 7]]\n",
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"X_train = pd.concat([X_train, Y_train], axis=1)\n",
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"X_train = X_train[6] + X_train[0] + X_train[7]\n",
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"X_train = X_train.apply(clean)\n",
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"vocab_size = 30000\n",
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"embed_size = 150\n",
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"Dataset = Bigrams(X_train, vocab_size)\n",
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"\n",
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"\n",
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"\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": 2,
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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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" Epoka 0--------------------------------------------------------\n"
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]
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},
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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/przemek/anaconda3/envs/env/lib/python3.9/site-packages/torch/nn/modules/container.py:141: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
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" input = module(input)\n"
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]
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},
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{
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"name": "stdout",
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"13600 tensor(5.7899, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"13700 tensor(5.7258, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"13800 tensor(5.9402, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"13900 tensor(5.8674, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"14000 tensor(5.7627, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"14100 tensor(5.8849, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"14200 tensor(5.7721, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"14300 tensor(5.7737, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"14400 tensor(5.7790, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"14500 tensor(5.8570, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"14600 tensor(5.8281, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"14700 tensor(5.7613, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"14800 tensor(5.8226, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"14900 tensor(5.7584, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"15000 tensor(5.7686, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"15100 tensor(5.8094, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"15200 tensor(5.7397, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"15300 tensor(5.7407, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"15400 tensor(5.5733, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"15500 tensor(5.5254, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"15600 tensor(5.7856, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"15700 tensor(5.6769, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"15800 tensor(5.5810, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"15900 tensor(5.8195, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"16000 tensor(5.8086, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"16100 tensor(5.8340, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"16200 tensor(5.8087, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"16300 tensor(5.8688, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"16400 tensor(5.6974, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"16500 tensor(5.8742, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
|
||||
"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
|
||||
"\n",
|
||||
"if(not exists('nn_model2.bin')):\n",
|
||||
" data = DataLoader(Dataset, batch_size=8000)\n",
|
||||
" optimizer = torch.optim.Adam(model.parameters())\n",
|
||||
" criterion = torch.nn.NLLLoss()\n",
|
||||
"\n",
|
||||
" model.train()\n",
|
||||
" step = 0\n",
|
||||
" for i in range(2):\n",
|
||||
" print(f\" Epoka {i}--------------------------------------------------------\")\n",
|
||||
" for x, y in data:\n",
|
||||
" x = x.to(device)\n",
|
||||
" y = y.to(device)\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" ypredicted = model(x)\n",
|
||||
" loss = criterion(torch.log(ypredicted), y)\n",
|
||||
" if step % 100 == 0:\n",
|
||||
" print(step, loss)\n",
|
||||
" step += 1\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()\n",
|
||||
"\n",
|
||||
" torch.save(model.state_dict(), 'nn_model2.bin')\n",
|
||||
"else:\n",
|
||||
" model.load_state_dict(torch.load('nn_model2.bin')) \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"vocab = Dataset.vocab\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# nltk.download('punkt')\n",
|
||||
"def predict_word(ws):\n",
|
||||
" ixs = torch.tensor(vocab.forward(ws)).to(device)\n",
|
||||
" out = model(ixs)\n",
|
||||
" top = torch.topk(out[0], 8)\n",
|
||||
" top_indices = top.indices.tolist()\n",
|
||||
" top_probs = top.values.tolist()\n",
|
||||
" top_words = vocab.lookup_tokens(top_indices)\n",
|
||||
" pred_str = \"\"\n",
|
||||
" for word, prob in list(zip(top_words, top_probs)):\n",
|
||||
" pred_str += f\"{word}:{prob} \"\n",
|
||||
"# pred_str += f':0.01'\n",
|
||||
" return pred_str\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def word_gap_prediction(file):\n",
|
||||
" X_test = pd.read_csv(f'{file}/in.tsv.xz', sep='\\t', header=None, quoting=csv.QUOTE_NONE, on_bad_lines='skip', encoding=\"UTF-8\")[6]\n",
|
||||
" X_test = X_test.apply(clean)\n",
|
||||
" with open(f'{file}/out.tsv', \"w+\", encoding=\"UTF-8\") as f:\n",
|
||||
" for row in X_test:\n",
|
||||
" result = {}\n",
|
||||
" before = None\n",
|
||||
" for before in get_words_from_line(clean(str(row)), False):\n",
|
||||
" pass\n",
|
||||
" before = [before]\n",
|
||||
" if(len(before) < 1):\n",
|
||||
" pred_str = \"a:0.2 the:0.2 to:0.2 of:0.1 and:0.1 of:0.1 :0.1\"\n",
|
||||
" else:\n",
|
||||
" pred_str = predict_word(before)\n",
|
||||
" pred_str = pred_str.strip()\n",
|
||||
" f.write(pred_str + \"\\n\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/przemek/anaconda3/envs/env/lib/python3.9/site-packages/torch/nn/modules/container.py:141: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
|
||||
" input = module(input)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"word_gap_prediction(\"dev-0/\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/przemek/anaconda3/envs/env/lib/python3.9/site-packages/torch/nn/modules/container.py:141: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
|
||||
" input = module(input)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"word_gap_prediction(\"test-A/\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.9.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
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14828
test-A/out.tsv
14828
test-A/out.tsv
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Load Diff
Loading…
Reference in New Issue