文本数据增强-同义词替换、随机交换、随机插入、随机删除_eda(sentence, alpha_sr=0.1, alpha_ri=0.1, alpha_rs

根据zhangy代码改写，主要针对千言问题匹配进行文本数据增强。

依赖安装

pip install jieba
pip install synonyms 

eda.py

import jieba
import synonyms
import random
from random import shuffle
 
random.seed(2019)
 
#停用词列表，默认使用哈工大停用词表
f = open('stopwords/hit_stopwords.txt')
stop_words = list()
for stop_word in f.readlines():
    stop_words.append(stop_word[:-1])
 
 
#考虑到与英文的不同，暂时搁置
#文本清理
'''
import re
def get_only_chars(line):
    #1.清除所有的数字
'''
 
 
########################################################################
# 同义词替换
# 替换一个语句中的n个单词为其同义词
########################################################################
def synonym_replacement(words, n):
    new_words = words.copy()
    random_word_list = list(set([word for word in words if word not in stop_words]))     
    random.shuffle(random_word_list)
    num_replaced = 0  
    for random_word in random_word_list:          
        synonyms = get_synonyms(random_word)
        if len(synonyms) >= 1:
            synonym = random.choice(synonyms)   
            new_words = [synonym if word == random_word else word for word in new_words]   
            num_replaced += 1
        if num_replaced >= n: 
            break
 
    sentence = ' '.join(new_words)
    new_words = sentence.split(' ')
 
    return new_words
 
def get_synonyms(word):
    return synonyms.nearby(word)[0]
 
 
########################################################################
# 随机插入
# 随机在语句中插入n个词
########################################################################
def random_insertion(words, n):
    new_words = words.copy()
    for _ in range(n):
        add_word(new_words)
    return new_words
 
def add_word(new_words):
    synonyms = []
    counter = 0    
    while len(synonyms) < 1:
        random_word = new_words[random.randint(0, len(new_words)-1)]
        synonyms = get_synonyms(random_word)
        counter += 1
        if counter >= 10:
            return
    random_synonym = random.choice(synonyms)
    random_idx = random.randint(0, len(new_words)-1)
    new_words.insert(random_idx, random_synonym)
 
 
########################################################################
# Random swap
# Randomly swap two words in the sentence n times
########################################################################
 
def random_swap(words, n):
    new_words = words.copy()
    for _ in range(n):
        new_words = swap_word(new_words)
    return new_words
 
def swap_word(new_words):
    random_idx_1 = random.randint(0, len(new_words)-1)
    random_idx_2 = random_idx_1
    counter = 0
    while random_idx_2 == random_idx_1:
        random_idx_2 = random.randint(0, len(new_words)-1)
        counter += 1
        if counter > 3:
            return new_words
    new_words[random_idx_1], new_words[random_idx_2] = new_words[random_idx_2], new_words[random_idx_1] 
    return new_words
 
########################################################################
# 随机删除
# 以概率p删除语句中的词
########################################################################
def random_deletion(words, p):
 
    if len(words) == 1:
        return words
 
    new_words = []
    for word in words:
        r = random.uniform(0, 1)
        if r > p:
            new_words.append(word)
 
    if len(new_words) == 0:
        rand_int = random.randint(0, len(words)-1)
        return [words[rand_int]]
 
    return new_words
 
 
########################################################################
#EDA函数
def eda(sentence, alpha_sr=0.1, alpha_ri=0.1, alpha_rs=0.1, p_rd=0.1, num_aug=9):
    seg_list = jieba.cut(sentence)
    seg_list = " ".join(seg_list)
    words = list(seg_list.split())
    num_words = len(words)
 
    augmented_sentences = []
    num_new_per_technique = int(num_aug/4)+1
    n_sr = max(1, int(alpha_sr * num_words))
    n_ri = max(1, int(alpha_ri * num_words))
    n_rs = max(1, int(alpha_rs * num_words))
 
    #print(words, "\n")
 
    
    #同义词替换sr
    for _ in range(num_new_per_technique):
        a_words = synonym_replacement(words, n_sr)
        augmented_sentences.append(' '.join(a_words))
 
    #随机插入ri
    for _ in range(num_new_per_technique):
        a_words = random_insertion(words, n_ri)
        augmented_sentences.append(' '.join(a_words))
    
    #随机交换rs
    for _ in range(num_new_per_technique):
        a_words = random_swap(words, n_rs)
        augmented_sentences.append(' '.join(a_words))
 
   
    #随机删除rd
    for _ in range(num_new_per_technique):
        a_words = random_deletion(words, p_rd)
        augmented_sentences.append(' '.join(a_words))
    
    #print(augmented_sentences)
    shuffle(augmented_sentences)
 
    if num_aug >= 1:
        augmented_sentences = augmented_sentences[:num_aug]
    else:
        keep_prob = num_aug / len(augmented_sentences)
        augmented_sentences = [s for s in augmented_sentences if random.uniform(0, 1) < keep_prob]
 
    augmented_sentences.append(seg_list)
 
    return augmented_sentences

augment.py

from eda import *
 
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("--input", required=True, type=str, help="原始数据的输入文件目录")
ap.add_argument("--output", required=False, type=str, help="增强数据后的输出文件目录")
ap.add_argument("--num_aug", required=False, type=int, help="每条原始语句增强的语句数")
ap.add_argument("--alpha", required=False, type=float, help="每条语句中将会被改变的单词数占比")
args = ap.parse_args()
 
#输出文件
output = None
if args.output:
    output = args.output
else:
    from os.path import dirname, basename, join
    output = join(dirname(args.input), 'eda_' + basename(args.input))
 
#每条原始语句增强的语句数
num_aug = 9 #default
if args.num_aug:
    num_aug = args.num_aug
 
#每条语句中将会被改变的单词数占比
alpha = 0.1 #default
if args.alpha:
    alpha = args.alpha
 
def gen_eda(train_orig, output_file, alpha, num_aug=9):
 
    writer = open(output_file, 'w')
    lines = open(train_orig, 'r').readlines()
 
    print("正在使用EDA生成增强语句...")
    for i, line in enumerate(lines):
        parts = line[:-1].split('\t')    #使用[:-1]是把\n去掉了
        sentence1 = parts[0]
        sentence2 = parts[1]
        label = parts[2]
        aug_sentences1 = eda(sentence1, alpha_sr=alpha, alpha_ri=alpha, alpha_rs=alpha, p_rd=alpha, num_aug=num_aug)
        aug_sentences2 = eda(sentence2, alpha_sr=alpha, alpha_ri=alpha, alpha_rs=alpha, p_rd=alpha, num_aug=num_aug)
        for i,aug_sentence1 in enumerate(aug_sentences1):
            writer.write(aug_sentence1.replace(' ','') + "\t" + aug_sentences2[i].replace(' ','') + "\t" + label +'\n')
    writer.close()
    print("已生成增强语句!")
    print(output_file)
 
if __name__ == "__main__":
    gen_eda(args.input, output, alpha=alpha, num_aug=num_aug)

增强训练数据集

!python augment.py --input train.txt