99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
import pandas as pd
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import csv
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from collections import Counter, defaultdict
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from nltk.tokenize import RegexpTokenizer
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from nltk import trigrams
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import regex as re
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import lzma
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import kenlm
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from math import log10
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from english_words import english_words_set
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class WordPred:
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def __init__(self):
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self.tokenizer = RegexpTokenizer(r"\w+")
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# self.model = defaultdict(lambda: defaultdict(lambda: 0))
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self.model = kenlm.Model("model.binary")
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self.words = set()
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def read_file(self, file):
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for line in file:
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text = line.split("\t")
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yield re.sub(r"[^\w\d'\s]+", '',
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re.sub(' +', ' ', ' '.join([text[6], text[7]]).replace("\\n", " ").replace("\n", "").lower()))
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def read_file_7(self, file):
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for line in file:
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text = line.split("\t")
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yield re.sub(r"[^\w\d'\s]+", '', re.sub(' +', ' ', text[7].replace("\\n", " ").replace("\n", "").lower()))
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def fill_words(self, file_path, output_file):
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with open(output_file, 'w') as out:
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with lzma.open(file_path, mode='rt') as file:
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for text in self.read_file(file):
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for mword in text.split(" "):
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if mword not in self.words:
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out.write(mword + "\n")
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self.words.add(mword)
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def read_words(self, file_path):
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with open(file_path, 'r') as fin:
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for word in fin.readlines():
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word = word.replace("\n", "")
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if word:
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self.words.add(word)
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def create_train_file(self, file_path, output_path, rows=10000):
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with open(output_path, 'w') as outputfile:
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with lzma.open(file_path, mode='rt') as file:
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for index, text in enumerate(self.read_file(file)):
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outputfile.write(text)
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if index == rows:
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break
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outputfile.close()
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def generate_outputs(self, input_file, output_file):
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with open(output_file, 'w') as outputf:
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with lzma.open(input_file, mode='rt') as file:
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for index, text in enumerate(self.read_file_7(file)):
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tokens = self.tokenizer.tokenize(text)
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if len(tokens) < 4:
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prediction = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'
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else:
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prediction = wp.predict_probs(tokens[0], tokens[1])
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outputf.write(prediction + '\n')
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def predict_probs(self, word1, word2):
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preds = []
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for word in english_words_set:
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sentence = word1 + ' ' + word + ' ' + word2
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words_score = self.model.score(sentence, bos=False, eos=False)
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if len(preds) < 12:
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preds.append((word, words_score))
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else:
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min_score = preds[0]
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for score in preds:
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if min_score[1] > score[1]:
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min_score = score
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if min_score[1] < words_score:
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preds.remove(min_score)
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preds.append((word, words_score))
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probs = sorted(preds, key=lambda sc: sc[1], reverse=True)
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str_prediction = ''
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for word, prob in probs:
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str_prediction += f'{word}:{prob} '
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str_prediction += f':{log10(0.99)}'
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return str_prediction
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if __name__ == "__main__":
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wp = WordPred()
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# wp.create_train_file("train/in.tsv.xz", "train/in.txt")
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# wp.fill_words("train/in.tsv.xz", "words.txt")
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# wp.read_words("words.txt")
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wp.generate_outputs("dev-0/in.tsv.xz", "dev-0/out3.tsv")
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wp.generate_outputs("test-A/in.tsv.xz", "test-A/out3.tsv")
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