bigram
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Maciej(Linux)
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run.py
Executable file → Normal file
232
run.py
Executable file → Normal file
@ -1,22 +1,68 @@
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#!/usr/bin/env python
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# coding: utf-8
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# In[2]:
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from nltk import trigrams, word_tokenize
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import pandas as pd
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import csv
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from itertools import islice
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import regex as re
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from collections import Counter, defaultdict
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import kenlm
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from english_words import english_words_alpha_set
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from math import log10
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import sys
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from torchtext.vocab import build_vocab_from_iterator
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from torch import nn
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import torch
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from torch.utils.data import IterableDataset
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import itertools
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import pandas as pd
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from torch.utils.data import DataLoader
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import csv
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def data_preprocessing(text):
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return re.sub(r'\p{P}', '', text.lower().replace('-\\n', '').replace('\\n', ' ').replace("'ll", " will").replace("-", "").replace("'ve", " have").replace("'s", " is"))
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def get_words_from_line(line):
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line = line.rstrip()
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yield '<s>'
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for m in re.finditer(r'[\p{L}0-9\*]+|\p{P}+', line):
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yield m.group(0).lower()
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yield '</s>'
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# In[3]:
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def get_word_lines_from_file(data):
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for line in data:
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yield get_words_from_line(line)
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class SimpleBigramNeuralLanguageModel(nn.Module):
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def __init__(self, vocabulary_size, embedding_size):
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super(SimpleBigramNeuralLanguageModel, self).__init__()
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self.model = nn.Sequential(
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nn.Embedding(vocabulary_size, embedding_size),
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nn.Linear(embedding_size, vocabulary_size),
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nn.Softmax()
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)
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def forward(self, x):
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return self.model(x)
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def look_ahead_iterator(gen):
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prev = None
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for item in gen:
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if prev is not None:
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yield (prev, item)
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prev = item
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class Bigrams(IterableDataset):
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def __init__(self, text_file, vocabulary_size):
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self.vocab = build_vocab_from_iterator(
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get_word_lines_from_file(text_file),
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max_tokens = vocabulary_size,
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specials = ['<unk>'])
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self.vocab.set_default_index(self.vocab['<unk>'])
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self.vocabulary_size = vocabulary_size
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self.text_file = text_file
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def __iter__(self):
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return look_ahead_iterator(
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(self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))
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in_file = 'train/in.tsv.xz'
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out_file = 'train/expected.tsv'
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train_set = pd.read_csv(
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'train/in.tsv.xz',
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sep='\t',
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@ -31,116 +77,72 @@ train_labels = pd.read_csv(
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quoting=csv.QUOTE_NONE,
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nrows=35000)
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# In[4]:
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data = pd.concat([train_set, train_labels], axis=1)
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# In[5]:
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data = train_set[6] + train_set[0] + train_set[7]
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# In[6]:
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def data_preprocessing(text):
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return re.sub(r'\p{P}', '', text.lower().replace('-\\n', '').replace('\\n', ' ').replace("'ll", " will").replace("-", "").replace("'ve", " have").replace("'s", " is"))
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# In[8]:
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data = data.apply(data_preprocessing)
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vocab_size = 30000
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embed_size = 150
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bigram_data = Bigrams(data, vocab_size)
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device = 'cpu'
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model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)
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data = DataLoader(bigram_data, batch_size=5000)
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optimizer = torch.optim.Adam(model.parameters())
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criterion = torch.nn.NLLLoss()
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model.train()
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step = 0
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for x, y in data:
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x = x.to(device)
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y = y.to(device)
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optimizer.zero_grad()
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ypredicted = model(x)
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loss = criterion(torch.log(ypredicted), y)
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if step % 100 == 0:
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print(step, loss)
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step += 1
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loss.backward()
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optimizer.step()
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torch.save(model.state_dict(), 'model1.bin')
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vocab = bigram_data.vocab
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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'
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# In[25]:
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with open("train_file.txt", "w+") as f:
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for text in data:
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f.write(text + "\n")
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# In[27]:
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KENLM_BUILD_PATH='../kenlm/build/bin/lmplz'
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# In[28]:
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get_ipython().system('$KENLM_BUILD_PATH -o 4 < train_file.txt > kenlm_model.arpa')
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# In[29]:
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import os
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print(os.getcwd())
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model = kenlm.Model('kenlm_model.arpa')
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# In[30]:
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def predict(before, after):
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result = ''
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prob = 0.0
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best = []
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for word in english_words_alpha_set:
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text = ' '.join([before, word, after])
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text_score = model.score(text, bos=False, eos=False)
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if len(best) < 12:
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best.append((word, text_score))
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else:
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is_better = False
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worst_score = None
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for score in best:
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if not worst_score:
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worst_score = score
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else:
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if worst_score[1] > score[1]:
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worst_score = score
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if worst_score[1] < text_score:
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best.remove(worst_score)
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best.append((word, text_score))
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probs = sorted(best, key=lambda tup: tup[1], reverse=True)
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pred_str = ''
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for word, prob in probs:
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pred_str += f'{word}:{prob} '
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pred_str += f':{log10(0.99)}'
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def predict_word(w):
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ixs = torch.tensor(vocab.forward(w)).to(device)
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out = model(ixs)
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top = torch.topk(out[0], 8)
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top_indices = top.indices.tolist()
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top_probs = top.values.tolist()
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top_words = vocab.lookup_tokens(top_indices)
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pred_str = ""
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for word, prob in list(zip(top_words, top_probs)):
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pred_str += f"{word}:{prob} "
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return pred_str
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# In[31]:
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def predict(f):
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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]
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x = x.apply(data_preprocessing)
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def make_prediction(path, result_path):
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data = pd.read_csv(path, sep='\t', header=None, quoting=csv.QUOTE_NONE)
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with open(result_path, 'w', encoding='utf-8') as file_out:
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for _, row in data.iterrows():
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before, after = word_tokenize(data_preprocessing(str(row[6]))), word_tokenize(data_preprocessing(str(row[7])))
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if len(before) < 2 or len(after) < 2:
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pred = prediction
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with open(f'{f}/out.tsv', "w+", encoding="UTF-8") as f:
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for row in x:
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result = {}
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before = None
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for before in get_words_from_line(data_preprocessing(str(row)), False):
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pass
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before = [before]
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if(len(before) < 1):
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pred_str = prediction
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else:
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pred = predict(before[-1], after[0])
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file_out.write(pred + '\n')
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pred_str = predict_word(before)
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pred_str = pred_str.strip()
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f.write(pred_str + "\n")
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# In[32]:
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make_prediction("dev-0/in.tsv.xz", "dev-0/out.tsv")
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# In[33]:
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make_prediction("test-A/in.tsv.xz", "test-A/out.tsv")
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prediction("dev-0/")
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prediction("test-A/")
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146
run3.py
Executable file
146
run3.py
Executable file
@ -0,0 +1,146 @@
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#!/usr/bin/env python
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# coding: utf-8
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# In[2]:
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from nltk import trigrams, word_tokenize
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import pandas as pd
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import csv
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import regex as re
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from collections import Counter, defaultdict
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import kenlm
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from english_words import english_words_alpha_set
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from math import log10
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# In[3]:
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train_set = pd.read_csv(
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'train/in.tsv.xz',
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sep='\t',
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header=None,
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quoting=csv.QUOTE_NONE,
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nrows=35000)
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train_labels = pd.read_csv(
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'train/expected.tsv',
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sep='\t',
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header=None,
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quoting=csv.QUOTE_NONE,
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nrows=35000)
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# In[4]:
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data = pd.concat([train_set, train_labels], axis=1)
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# In[5]:
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data = train_set[6] + train_set[0] + train_set[7]
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# In[6]:
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def data_preprocessing(text):
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return re.sub(r'\p{P}', '', text.lower().replace('-\\n', '').replace('\\n', ' ').replace("'ll", " will").replace("-", "").replace("'ve", " have").replace("'s", " is"))
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# In[8]:
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data = data.apply(data_preprocessing)
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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'
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# In[25]:
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with open("train_file.txt", "w+") as f:
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for text in data:
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f.write(text + "\n")
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# In[27]:
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KENLM_BUILD_PATH='../kenlm/build/bin/lmplz'
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# In[28]:
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get_ipython().system('$KENLM_BUILD_PATH -o 4 < train_file.txt > kenlm_model.arpa')
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# In[29]:
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import os
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print(os.getcwd())
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model = kenlm.Model('kenlm_model.arpa')
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# In[30]:
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def predict(before, after):
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result = ''
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prob = 0.0
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best = []
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for word in english_words_alpha_set:
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text = ' '.join([before, word, after])
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text_score = model.score(text, bos=False, eos=False)
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if len(best) < 12:
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best.append((word, text_score))
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else:
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is_better = False
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worst_score = None
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for score in best:
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if not worst_score:
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worst_score = score
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else:
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if worst_score[1] > score[1]:
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worst_score = score
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if worst_score[1] < text_score:
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best.remove(worst_score)
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best.append((word, text_score))
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probs = sorted(best, key=lambda tup: tup[1], reverse=True)
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pred_str = ''
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for word, prob in probs:
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pred_str += f'{word}:{prob} '
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pred_str += f':{log10(0.99)}'
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return pred_str
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# In[31]:
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def make_prediction(path, result_path):
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data = pd.read_csv(path, sep='\t', header=None, quoting=csv.QUOTE_NONE)
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with open(result_path, 'w', encoding='utf-8') as file_out:
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for _, row in data.iterrows():
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before, after = word_tokenize(data_preprocessing(str(row[6]))), word_tokenize(data_preprocessing(str(row[7])))
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if len(before) < 2 or len(after) < 2:
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pred = prediction
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else:
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pred = predict(before[-1], after[0])
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file_out.write(pred + '\n')
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# In[32]:
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make_prediction("dev-0/in.tsv.xz", "dev-0/out.tsv")
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# In[33]:
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make_prediction("test-A/in.tsv.xz", "test-A/out.tsv")
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