init solution trigram. score 580

This commit is contained in:
kpierzynski 2024-05-14 17:10:07 +02:00
commit b13b78e58e
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*~
*.swp
*.bak
*.pyc
*.o
.DS_Store
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README.md Normal file
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## challenging-america-word-gap-prediction
### using simple trigram nn
calculated perplexity: 583.35

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config.txt Normal file
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--metric PerplexityHashed --precision 2 --in-header in-header.tsv --out-header out-header.tsv

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#!/usr/bin/env python
# coding: utf-8
# In[9]:
import regex as re
from tqdm.notebook import tqdm
def _clean(text):
text = text.replace('-\\n', '').replace('\\n', ' ').replace('\\t', ' ')#.replace('<s>','s')
while ' ' in text:
text = text.replace(' ',' ')
return re.sub(r'\p{P}', '', text)
def clean(text):
text = text.replace('-\\n', '').replace('\\n', ' ').replace('\\t', ' ')
text = re.sub(r'\n', ' ', text)
text = re.sub(r'(?<=\w)[,-](?=\w)', '', text)
text = re.sub(r'\s+', ' ', text)
text = re.sub(r'\p{P}', '', text)
text = text.strip()
return text
def generate_file(input_path, expected_path, output_path):
with open(input_path, encoding='utf8') as input_file, open(expected_path, encoding='utf8') as expected_file, open(output_path, 'w', encoding='utf-8') as output_file:
for line, word in tqdm(zip(input_file, expected_file), total=432022):
columns = line.split('\t')
prefix = clean(columns[6])
suffix = clean(columns[7])
train_line = f"{prefix.strip()} {word.strip()} {suffix.strip()}\n"
output_file.write(train_line)
generate_file('train/in.tsv', 'train/expected.tsv', 'train/train.txt')
# In[ ]:

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in-header.tsv Normal file
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FileId Year LeftContext RightContext
1 FileId Year LeftContext RightContext

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Word
1 Word

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run.py Normal file
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
from torch.utils.data import IterableDataset, DataLoader
from torchtext.vocab import build_vocab_from_iterator
import regex as re
import sys
import itertools
from itertools import islice
from torch import nn
import torch
from tqdm.notebook import tqdm
embed_size = 100
vocab_size = 25_000
num_epochs = 1
device = 'cuda'
batch_size = 2048
train_file_path = 'train/train.txt'
with open(train_file_path, 'r', encoding='utf-8') as file:
total = len(file.readlines())
# In[2]:
# Function to extract words from a line of text
def get_words_from_line(line):
line = line.rstrip()
yield '<s>'
for m in re.finditer(r'[\p{L}0-9\*]+|\p{P}+', line):
yield m.group(0).lower()
yield '</s>'
# Generator to read lines from a file
def get_word_lines_from_file(file_name):
limit = total * 2
with open(file_name, 'r', encoding='utf8') as fh:
for line in tqdm(fh, total=total):
limit -= 1
if not limit:
break
yield get_words_from_line(line)
# Function to create trigrams from a sequence
def look_ahead_iterator(gen):
prev1, prev2 = None, None
for item in gen:
if prev1 is not None and prev2 is not None:
yield (prev2, prev1, item)
prev2 = prev1
prev1 = item
# Dataset class for trigrams
class Trigrams(IterableDataset):
def __init__(self, text_file, vocabulary_size):
self.vocab = build_vocab_from_iterator(
get_word_lines_from_file(text_file),
max_tokens=vocabulary_size,
specials=['<unk>']
)
self.vocab.set_default_index(self.vocab['<unk>'])
self.vocabulary_size = vocabulary_size
self.text_file = text_file
def __iter__(self):
return look_ahead_iterator(
(self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file)))
)
# Instantiate the dataset
train_dataset = Trigrams(train_file_path, vocab_size)
# In[3]:
# Neural network model for trigram language modeling
class SimpleTrigramNeuralLanguageModel(nn.Module):
def __init__(self, vocabulary_size, embedding_size):
super(SimpleTrigramNeuralLanguageModel, self).__init__()
self.embedding = nn.Embedding(vocabulary_size, embedding_size)
self.linear1 = nn.Linear(embedding_size * 2, embedding_size)
self.linear2 = nn.Linear(embedding_size, vocabulary_size)
self.softmax = nn.Softmax(dim=1)
self.embedding_size = embedding_size
def forward(self, x):
embeds = self.embedding(x).view(-1, self.embedding_size * 2)
out = torch.relu(self.linear1(embeds))
out = self.linear2(out)
return self.softmax(out)
# Instantiate the model
model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size)
# In[4]:
model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size).to(device)
data = DataLoader(train_dataset, batch_size=batch_size)
optimizer = torch.optim.Adam(model.parameters())
criterion = torch.nn.NLLLoss()
model.train()
step = 0
for _ in range(num_epochs):
for x1,x2,y in tqdm(data, total=total):
x = torch.cat((x1,x2), dim=0).to(device)
y = y.to(device)
optimizer.zero_grad()
ypredicted = model(x)
loss = criterion(torch.log(ypredicted), y)
if step % 5000 == 0:
print(step, loss)
step += 1
loss.backward()
optimizer.step()
model.eval()
# In[10]:
def get_gap_candidates(words, n=10, vocab=train_dataset.vocab):
ixs = vocab.forward(words)
ixs = torch.tensor(ixs)
ixs = torch.cat(tuple([ixs]), dim=0).to(device)
out = model(ixs)
top = torch.topk(out[0], n)
top_indices = top.indices.tolist()
top_probs = top.values.tolist()
top_words = vocab.lookup_tokens(top_indices)
return list(zip(top_words, top_probs))
# In[11]:
def clean(text):
text = text.replace('-\\n', '').replace('\\n', ' ').replace('\\t', ' ')
text = re.sub(r'\n', ' ', text)
text = re.sub(r'(?<=\w)[,-](?=\w)', '', text)
text = re.sub(r'\s+', ' ', text)
text = re.sub(r'\p{P}', '', text)
text = text.strip()
return text
def predictor(prefix):
words = clean(prefix)
candidates = get_gap_candidates(words.strip().split(' ')[-2:])
probs_sum = 0
output = ''
for word,prob in candidates:
if word == "<unk>":
continue
probs_sum += prob
output += f"{word}:{prob} "
output += f":{1-probs_sum}"
return output
# In[12]:
predictor("I really bug")
# In[13]:
def generate_result(input_path, output_path='out.tsv'):
with open(input_path, encoding='utf-8') as f:
lines = f.readlines()
with open(output_path, 'w', encoding='utf-8') as output_file:
for line in lines:
result = predictor(line)
output_file.write(result + '\n')
# In[14]:
generate_result('dev-0/in.tsv', output_path='dev-0/out.tsv')
# In[ ]:

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{
"cells": [
{
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"id": "dfd117bd-5d6f-46e6-979c-092a8065fa0b",
"metadata": {},
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{
"name": "stderr",
"output_type": "stream",
"text": [
"S:\\WENV\\Lib\\site-packages\\torchtext\\vocab\\__init__.py:4: UserWarning: \n",
"/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
"Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
" warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n",
"S:\\WENV\\Lib\\site-packages\\torchtext\\utils.py:4: UserWarning: \n",
"/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
"Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
" warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n"
]
}
],
"source": [
"from torch.utils.data import IterableDataset, DataLoader\n",
"from torchtext.vocab import build_vocab_from_iterator\n",
"\n",
"import regex as re\n",
"import sys\n",
"import itertools\n",
"from itertools import islice\n",
"\n",
"from torch import nn\n",
"import torch\n",
"\n",
"from tqdm.notebook import tqdm\n",
"\n",
"embed_size = 100\n",
"vocab_size = 25_000\n",
"num_epochs = 1\n",
"device = 'cuda'\n",
"batch_size = 2048\n",
"train_file_path = 'train/train.txt'\n",
"\n",
"with open(train_file_path, 'r', encoding='utf-8') as file:\n",
" total = len(file.readlines())"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "40392665-79bc-4032-a5de-9d189545c9f7",
"metadata": {},
"outputs": [
{
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"text/plain": [
" 0%| | 0/432022 [00:00<?, ?it/s]"
]
},
"metadata": {},
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}
],
"source": [
"# Function to extract words from a line of text\n",
"def get_words_from_line(line):\n",
" line = line.rstrip()\n",
" yield '<s>'\n",
" for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n",
" yield m.group(0).lower()\n",
" yield '</s>'\n",
"\n",
"# Generator to read lines from a file\n",
"def get_word_lines_from_file(file_name):\n",
" limit = total * 2\n",
" with open(file_name, 'r', encoding='utf8') as fh:\n",
" for line in tqdm(fh, total=total):\n",
" limit -= 1\n",
" if not limit:\n",
" break\n",
" yield get_words_from_line(line)\n",
"\n",
"# Function to create trigrams from a sequence\n",
"def look_ahead_iterator(gen):\n",
" prev1, prev2 = None, None\n",
" for item in gen:\n",
" if prev1 is not None and prev2 is not None:\n",
" yield (prev2, prev1, item)\n",
" prev2 = prev1\n",
" prev1 = item\n",
"\n",
"# Dataset class for trigrams\n",
"class Trigrams(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=['<unk>']\n",
" )\n",
" self.vocab.set_default_index(self.vocab['<unk>'])\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",
"\n",
"# Instantiate the dataset\n",
"train_dataset = Trigrams(train_file_path, vocab_size)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0cf7aa68-37aa-48a4-b647-e0e5002ca5c9",
"metadata": {},
"outputs": [],
"source": [
"# Neural network model for trigram language modeling\n",
"class SimpleTrigramNeuralLanguageModel(nn.Module):\n",
" def __init__(self, vocabulary_size, embedding_size):\n",
" super(SimpleTrigramNeuralLanguageModel, self).__init__()\n",
" self.embedding = nn.Embedding(vocabulary_size, embedding_size)\n",
" self.linear1 = nn.Linear(embedding_size * 2, embedding_size)\n",
" self.linear2 = nn.Linear(embedding_size, vocabulary_size)\n",
" self.softmax = nn.Softmax(dim=1)\n",
" self.embedding_size = embedding_size\n",
"\n",
" def forward(self, x):\n",
" embeds = self.embedding(x).view(-1, self.embedding_size * 2)\n",
" out = torch.relu(self.linear1(embeds))\n",
" out = self.linear2(out)\n",
" return self.softmax(out)\n",
"\n",
"# Instantiate the model\n",
"model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0858967e-5143-4253-921d-a009dbbdca27",
"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"0 tensor(10.1654, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"5000 tensor(6.5147, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"10000 tensor(6.6747, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"15000 tensor(6.9061, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"20000 tensor(6.8899, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"25000 tensor(6.8373, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"30000 tensor(6.8942, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"35000 tensor(6.9564, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"40000 tensor(6.9709, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"45000 tensor(6.9592, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"50000 tensor(6.8195, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"55000 tensor(6.7074, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"60000 tensor(6.8755, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
"65000 tensor(6.9605, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
]
},
{
"data": {
"text/plain": [
"SimpleTrigramNeuralLanguageModel(\n",
" (embedding): Embedding(25000, 100)\n",
" (linear1): Linear(in_features=200, out_features=100, bias=True)\n",
" (linear2): Linear(in_features=100, out_features=25000, bias=True)\n",
" (softmax): Softmax(dim=1)\n",
")"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
"data = DataLoader(train_dataset, batch_size=batch_size)\n",
"optimizer = torch.optim.Adam(model.parameters())\n",
"criterion = torch.nn.NLLLoss()\n",
"\n",
"model.train()\n",
"step = 0\n",
"for _ in range(num_epochs):\n",
" for x1,x2,y in tqdm(data, total=total):\n",
" x = torch.cat((x1,x2), dim=0).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 % 5000 == 0:\n",
" print(step, loss)\n",
" step += 1\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "da4d116c-beec-436d-84d8-577282507226",
"metadata": {},
"outputs": [],
"source": [
"def get_gap_candidates(words, n=10, vocab=train_dataset.vocab):\n",
" ixs = vocab.forward(words)\n",
" ixs = torch.tensor(ixs)\n",
" ixs = torch.cat(tuple([ixs]), dim=0).to(device)\n",
"\n",
" out = model(ixs)\n",
" top = torch.topk(out[0], n)\n",
" top_indices = top.indices.tolist()\n",
" top_probs = top.values.tolist()\n",
" top_words = vocab.lookup_tokens(top_indices)\n",
" return list(zip(top_words, top_probs))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "0cafd70a-29b3-4a49-b40f-b8ce3143084a",
"metadata": {},
"outputs": [],
"source": [
"def clean(text):\n",
" text = text.replace('-\\\\n', '').replace('\\\\n', ' ').replace('\\\\t', ' ')\n",
" text = re.sub(r'\\n', ' ', text)\n",
" text = re.sub(r'(?<=\\w)[,-](?=\\w)', '', text)\n",
" text = re.sub(r'\\s+', ' ', text)\n",
" text = re.sub(r'\\p{P}', '', text)\n",
" text = text.strip()\n",
" return text\n",
" \n",
"def predictor(prefix):\n",
" words = clean(prefix)\n",
" candidates = get_gap_candidates(words.strip().split(' ')[-2:])\n",
"\n",
" probs_sum = 0\n",
" output = ''\n",
" for word,prob in candidates:\n",
" if word == \"<unk>\":\n",
" continue\n",
" probs_sum += prob\n",
" output += f\"{word}:{prob} \"\n",
" output += f\":{1-probs_sum}\"\n",
"\n",
" return output"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "2084fb5f-6405-4e44-a06f-953db852e526",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'the:0.07267232984304428 of:0.043321046978235245 and:0.032147664576768875 to:0.02692588046193123 a:0.020654045045375824 in:0.020213929936289787 that:0.010836434550583363 is:0.00959325022995472 it:0.008407277055084705 :0.755228141322732'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictor(\"I really bug\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "965ebaf3-4c0b-4462-8ac5-4746ec9489ab",
"metadata": {},
"outputs": [],
"source": [
"def generate_result(input_path, output_path='out.tsv'):\n",
" with open(input_path, encoding='utf-8') as f:\n",
" lines = f.readlines()\n",
"\n",
" with open(output_path, 'w', encoding='utf-8') as output_file:\n",
" for line in lines:\n",
" result = predictor(line)\n",
" output_file.write(result + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "80547ba7-9d01-4d2b-9e83-269919513de9",
"metadata": {},
"outputs": [],
"source": [
"generate_result('dev-0/in.tsv', output_path='dev-0/out.tsv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1d7d72b0-d629-487e-bcec-4756fa88ae49",
"metadata": {},
"outputs": [],
"source": []
}
],
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