This commit is contained in:
Maciej(Linux) 2022-05-08 20:39:15 +02:00
parent 9d519941b0
commit b22b9c3534
2 changed files with 263 additions and 115 deletions

232
run.py Executable file → Normal file
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@ -1,22 +1,68 @@
#!/usr/bin/env python from itertools import islice
# coding: utf-8
# In[2]:
from nltk import trigrams, word_tokenize
import pandas as pd
import csv
import regex as re import regex as re
from collections import Counter, defaultdict import sys
import kenlm from torchtext.vocab import build_vocab_from_iterator
from english_words import english_words_alpha_set from torch import nn
from math import log10 import torch
from torch.utils.data import IterableDataset
import itertools
import pandas as pd
from torch.utils.data import DataLoader
import csv
def data_preprocessing(text):
return re.sub(r'\p{P}', '', text.lower().replace('-\\n', '').replace('\\n', ' ').replace("'ll", " will").replace("-", "").replace("'ve", " have").replace("'s", " is"))
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>'
# In[3]: def get_word_lines_from_file(data):
for line in data:
yield get_words_from_line(line)
class SimpleBigramNeuralLanguageModel(nn.Module):
def __init__(self, vocabulary_size, embedding_size):
super(SimpleBigramNeuralLanguageModel, self).__init__()
self.model = nn.Sequential(
nn.Embedding(vocabulary_size, embedding_size),
nn.Linear(embedding_size, vocabulary_size),
nn.Softmax()
)
def forward(self, x):
return self.model(x)
def look_ahead_iterator(gen):
prev = None
for item in gen:
if prev is not None:
yield (prev, item)
prev = item
class Bigrams(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))))
in_file = 'train/in.tsv.xz'
out_file = 'train/expected.tsv'
train_set = pd.read_csv( train_set = pd.read_csv(
'train/in.tsv.xz', 'train/in.tsv.xz',
sep='\t', sep='\t',
@ -31,116 +77,72 @@ train_labels = pd.read_csv(
quoting=csv.QUOTE_NONE, quoting=csv.QUOTE_NONE,
nrows=35000) nrows=35000)
# In[4]:
data = pd.concat([train_set, train_labels], axis=1) data = pd.concat([train_set, train_labels], axis=1)
# In[5]:
data = train_set[6] + train_set[0] + train_set[7] data = train_set[6] + train_set[0] + train_set[7]
# In[6]:
def data_preprocessing(text):
return re.sub(r'\p{P}', '', text.lower().replace('-\\n', '').replace('\\n', ' ').replace("'ll", " will").replace("-", "").replace("'ve", " have").replace("'s", " is"))
# In[8]:
data = data.apply(data_preprocessing) data = data.apply(data_preprocessing)
vocab_size = 30000
embed_size = 150
bigram_data = Bigrams(data, vocab_size)
device = 'cpu'
model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)
data = DataLoader(bigram_data, batch_size=5000)
optimizer = torch.optim.Adam(model.parameters())
criterion = torch.nn.NLLLoss()
model.train()
step = 0
for x, y in data:
x = x.to(device)
y = y.to(device)
optimizer.zero_grad()
ypredicted = model(x)
loss = criterion(torch.log(ypredicted), y)
if step % 100 == 0:
print(step, loss)
step += 1
loss.backward()
optimizer.step()
torch.save(model.state_dict(), 'model1.bin')
vocab = bigram_data.vocab
prediction = 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77' prediction = 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77'
def predict_word(w):
# In[25]: ixs = torch.tensor(vocab.forward(w)).to(device)
out = model(ixs)
top = torch.topk(out[0], 8)
with open("train_file.txt", "w+") as f: top_indices = top.indices.tolist()
for text in data: top_probs = top.values.tolist()
f.write(text + "\n") top_words = vocab.lookup_tokens(top_indices)
pred_str = ""
for word, prob in list(zip(top_words, top_probs)):
# In[27]: pred_str += f"{word}:{prob} "
KENLM_BUILD_PATH='../kenlm/build/bin/lmplz'
# In[28]:
get_ipython().system('$KENLM_BUILD_PATH -o 4 < train_file.txt > kenlm_model.arpa')
# In[29]:
import os
print(os.getcwd())
model = kenlm.Model('kenlm_model.arpa')
# In[30]:
def predict(before, after):
result = ''
prob = 0.0
best = []
for word in english_words_alpha_set:
text = ' '.join([before, word, after])
text_score = model.score(text, bos=False, eos=False)
if len(best) < 12:
best.append((word, text_score))
else:
is_better = False
worst_score = None
for score in best:
if not worst_score:
worst_score = score
else:
if worst_score[1] > score[1]:
worst_score = score
if worst_score[1] < text_score:
best.remove(worst_score)
best.append((word, text_score))
probs = sorted(best, key=lambda tup: tup[1], reverse=True)
pred_str = ''
for word, prob in probs:
pred_str += f'{word}:{prob} '
pred_str += f':{log10(0.99)}'
return pred_str return pred_str
# In[31]: def predict(f):
x = pd.read_csv(f'{f}/in.tsv.xz', sep='\t', header=None, quoting=csv.QUOTE_NONE, on_bad_lines='skip', encoding="UTF-8")[6]
x = x.apply(data_preprocessing)
with open(f'{f}/out.tsv', "w+", encoding="UTF-8") as f:
def make_prediction(path, result_path): for row in x:
data = pd.read_csv(path, sep='\t', header=None, quoting=csv.QUOTE_NONE) result = {}
with open(result_path, 'w', encoding='utf-8') as file_out: before = None
for _, row in data.iterrows(): for before in get_words_from_line(data_preprocessing(str(row)), False):
before, after = word_tokenize(data_preprocessing(str(row[6]))), word_tokenize(data_preprocessing(str(row[7]))) pass
if len(before) < 2 or len(after) < 2: before = [before]
pred = prediction if(len(before) < 1):
pred_str = prediction
else: else:
pred = predict(before[-1], after[0]) pred_str = predict_word(before)
file_out.write(pred + '\n')
pred_str = pred_str.strip()
f.write(pred_str + "\n")
# In[32]: prediction("dev-0/")
prediction("test-A/")
make_prediction("dev-0/in.tsv.xz", "dev-0/out.tsv")
# In[33]:
make_prediction("test-A/in.tsv.xz", "test-A/out.tsv")

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run3.py Executable file
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@ -0,0 +1,146 @@
#!/usr/bin/env python
# coding: utf-8
# In[2]:
from nltk import trigrams, word_tokenize
import pandas as pd
import csv
import regex as re
from collections import Counter, defaultdict
import kenlm
from english_words import english_words_alpha_set
from math import log10
# In[3]:
train_set = pd.read_csv(
'train/in.tsv.xz',
sep='\t',
header=None,
quoting=csv.QUOTE_NONE,
nrows=35000)
train_labels = pd.read_csv(
'train/expected.tsv',
sep='\t',
header=None,
quoting=csv.QUOTE_NONE,
nrows=35000)
# In[4]:
data = pd.concat([train_set, train_labels], axis=1)
# In[5]:
data = train_set[6] + train_set[0] + train_set[7]
# In[6]:
def data_preprocessing(text):
return re.sub(r'\p{P}', '', text.lower().replace('-\\n', '').replace('\\n', ' ').replace("'ll", " will").replace("-", "").replace("'ve", " have").replace("'s", " is"))
# In[8]:
data = data.apply(data_preprocessing)
prediction = 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77'
# In[25]:
with open("train_file.txt", "w+") as f:
for text in data:
f.write(text + "\n")
# In[27]:
KENLM_BUILD_PATH='../kenlm/build/bin/lmplz'
# In[28]:
get_ipython().system('$KENLM_BUILD_PATH -o 4 < train_file.txt > kenlm_model.arpa')
# In[29]:
import os
print(os.getcwd())
model = kenlm.Model('kenlm_model.arpa')
# In[30]:
def predict(before, after):
result = ''
prob = 0.0
best = []
for word in english_words_alpha_set:
text = ' '.join([before, word, after])
text_score = model.score(text, bos=False, eos=False)
if len(best) < 12:
best.append((word, text_score))
else:
is_better = False
worst_score = None
for score in best:
if not worst_score:
worst_score = score
else:
if worst_score[1] > score[1]:
worst_score = score
if worst_score[1] < text_score:
best.remove(worst_score)
best.append((word, text_score))
probs = sorted(best, key=lambda tup: tup[1], reverse=True)
pred_str = ''
for word, prob in probs:
pred_str += f'{word}:{prob} '
pred_str += f':{log10(0.99)}'
return pred_str
# In[31]:
def make_prediction(path, result_path):
data = pd.read_csv(path, sep='\t', header=None, quoting=csv.QUOTE_NONE)
with open(result_path, 'w', encoding='utf-8') as file_out:
for _, row in data.iterrows():
before, after = word_tokenize(data_preprocessing(str(row[6]))), word_tokenize(data_preprocessing(str(row[7])))
if len(before) < 2 or len(after) < 2:
pred = prediction
else:
pred = predict(before[-1], after[0])
file_out.write(pred + '\n')
# In[32]:
make_prediction("dev-0/in.tsv.xz", "dev-0/out.tsv")
# In[33]:
make_prediction("test-A/in.tsv.xz", "test-A/out.tsv")