From cf92fd9b739050839f1edc31987e7111005be095 Mon Sep 17 00:00:00 2001 From: Jakub Adamski Date: Sun, 23 Apr 2023 09:10:51 +0200 Subject: [PATCH] trigram solution & moved previous implementations --- 07_bigram_regression.ipynb | 265 - bigram.py | 80 - dev-0/out.tsv | 20898 +++++++++++++++++------------------ simple.py | 24 - test-A/out.tsv | 14586 ++++++++++++------------ 5 files changed, 17742 insertions(+), 18111 deletions(-) delete mode 100644 07_bigram_regression.ipynb delete mode 100644 bigram.py delete mode 100644 simple.py diff --git a/07_bigram_regression.ipynb b/07_bigram_regression.ipynb deleted file mode 100644 index 4c7cb5a..0000000 --- a/07_bigram_regression.ipynb +++ /dev/null @@ -1,265 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Zadanie 1\n", - "Wyucz prosty bigramowy model języka oparty na regresji logistycznej (jak przedstawiono na wykładzie)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from itertools import islice\n", - "import regex as re\n", - "import sys\n", - "from torchtext.vocab import build_vocab_from_iterator\n", - "\n", - "\n", - "def get_words_from_line(line):\n", - " line = line.rstrip()\n", - " yield ''\n", - " for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n", - " yield m.group(0).lower()\n", - " yield ''\n", - "\n", - "\n", - "def get_word_lines_from_file(file_name):\n", - " with open(file_name, 'r') as fh:\n", - " for line in fh:\n", - " yield get_words_from_line(line)\n", - "\n", - "vocab_size = 20000\n", - "\n", - "vocab = build_vocab_from_iterator(\n", - " get_word_lines_from_file('test-A/in.tsv'),\n", - " max_tokens = vocab_size,\n", - " specials = [''])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3798" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vocab['welcome']" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(5.5038e-05, grad_fn=)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from torch import nn\n", - "import torch\n", - "\n", - "embed_size = 100\n", - "\n", - "class SimpleBigramNeuralLanguageModel(nn.Module):\n", - " def __init__(self, vocabulary_size, embedding_size):\n", - " super(SimpleBigramNeuralLanguageModel, self).__init__()\n", - " self.model = nn.Sequential(\n", - " nn.Embedding(vocabulary_size, embedding_size),\n", - " nn.Linear(embedding_size, vocabulary_size),\n", - " nn.Softmax()\n", - " )\n", - "\n", - " def forward(self, x):\n", - " return self.model(x)\n", - "\n", - "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size)\n", - "\n", - "vocab.set_default_index(vocab[''])\n", - "ixs = torch.tensor(vocab.forward(['welcone']))\n", - "out = model(ixs)\n", - "out[0][vocab['to']]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from torch.utils.data import IterableDataset\n", - "import itertools\n", - "\n", - "def look_ahead_iterator(gen):\n", - " prev = None\n", - " for item in gen:\n", - " if prev is not None:\n", - " yield (prev, item)\n", - " prev = item\n", - "\n", - "class Bigrams(IterableDataset):\n", - " def __init__(self, text_file, vocabulary_size):\n", - " self.vocab = build_vocab_from_iterator(\n", - " get_word_lines_from_file(text_file),\n", - " max_tokens = vocabulary_size,\n", - " specials = [''])\n", - " self.vocab.set_default_index(self.vocab[''])\n", - " self.vocabulary_size = vocabulary_size\n", - " self.text_file = text_file\n", - "\n", - " def __iter__(self):\n", - " return look_ahead_iterator(\n", - " (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))\n", - "\n", - "train_dataset = Bigrams('test-A/in.tsv', vocab_size)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 tensor(10.0928, grad_fn=)\n", - "100 tensor(8.4572, grad_fn=)\n", - "200 tensor(7.6165, grad_fn=)\n", - "300 tensor(6.9356, grad_fn=)\n", - "400 tensor(6.5687, grad_fn=)\n", - "500 tensor(6.2197, grad_fn=)\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "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", - "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", - "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", - "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", - "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", - "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", - "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", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "from torch.utils.data import DataLoader\n", - "\n", - "device = 'cpu' # cuda\n", - "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n", - "data = DataLoader(train_dataset, batch_size=5000)\n", - "optimizer = torch.optim.Adam(model.parameters())\n", - "criterion = torch.nn.NLLLoss()\n", - "\n", - "model.train()\n", - "step = 0\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(), 'model1.bin')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('liquid', 6933, 0.0004737793351523578),\n", - " ('bia', 5842, 0.00043268679291941226),\n", - " ('sole', 6386, 0.0004295798426028341),\n", - " ('nmeant', 17711, 0.00034942160709761083),\n", - " ('savs', 16709, 0.00034736539237201214),\n", - " ('striving', 12414, 0.0003441996523179114),\n", - " ('nol', 2640, 0.00032789510441944003),\n", - " ('imposing', 8457, 0.0003199590719304979),\n", - " ('hound', 17348, 0.00031824613688513637),\n", - " ('?\"\\\\', 4294, 0.0003141215711366385)]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "device = 'cpu' # cuda\n", - "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n", - "#model.load_state_dict(torch.load('model1.bin'))\n", - "model.eval()\n", - "\n", - "ixs = torch.tensor(vocab.forward(['welcome'])).to(device)\n", - "\n", - "out = model(ixs)\n", - "top = torch.topk(out[0], 10)\n", - "top_indices = top.indices.tolist()\n", - "top_probs = top.values.tolist()\n", - "top_words = vocab.lookup_tokens(top_indices)\n", - "list(zip(top_words, top_indices, top_probs))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "modelowanie", - "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": "3.10.10" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/bigram.py b/bigram.py deleted file mode 100644 index 781ce30..0000000 --- a/bigram.py +++ /dev/null @@ -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") - diff --git a/dev-0/out.tsv b/dev-0/out.tsv index 44f3585..813d243 100644 --- a/dev-0/out.tsv +++ b/dev-0/out.tsv @@ -1,10519 +1,10519 @@ -the:0.6 which:0.2 his:0.1 :0.1 -own:0.6 way:0.2 use:0.1 :0.1 -the:0.6 a:0.2 tho:0.1 :0.1 -have:0.5 too:0.2 it:0.1 :0.2 -pany:0.6 mittee:0.2 mon:0.1 :0.1 -the:0.6 a:0.2 his:0.1 :0.1 -with:0.6 so:0.2 association:0.1 :0.1 -a:0.5 in:0.2 to:0.1 :0.2 -have:0.5 too:0.2 it:0.1 :0.2 -that:0.6 a:0.2 like:0.1 :0.1 -are:0.6 two:0.2 men:0.1 :0.1 -the:0.6 be:0.2 a:0.1 :0.1 -much:0.6 late:0.2 many:0.1 :0.1 -the:0.6 he:0.2 i:0.1 :0.1 -to:0.6 and:0.2 that:0.1 :0.1 -and:0.6 in:0.2 is:0.1 :0.1 -for:0.6 that:0.2 and:0.1 :0.1 -as:0.6 known:0.2 and:0.1 :0.1 -0:0.6 among:0.2 ton:0.1 :0.1 -the:0.6 a:0.2 that:0.1 :0.1 -er:0.6 nificent:0.2 ian:0.1 :0.1 -and:0.6 is:0.2 in:0.1 :0.1 -the:0.6 a:0.2 that:0.1 :0.1 -and:0.6 city:0.2 to:0.1 :0.1 -made:0.6 in:0.2 and:0.1 :0.1 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+I:0.5 that:0.2 for:0.1 :0.2 diff --git a/simple.py b/simple.py deleted file mode 100644 index c9a0297..0000000 --- a/simple.py +++ /dev/null @@ -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" \ No newline at end of file diff --git a/test-A/out.tsv b/test-A/out.tsv index 513eefe..666947c 100644 --- a/test-A/out.tsv +++ b/test-A/out.tsv @@ 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