数千个文本文件的高效模糊字符串比较

Question

我需要在几千个纯文本文件中搜索一组名称。我正在生成三元组来保留上下文。我需要解释一些小的拼写错误，所以我使用Levenshtein距离计算，函数lev（）。最后一次命中的结果是命中一个名。我的python程序按预期工作，但速度非常慢。我正在寻找一种更快的方法来完成这个搜索，最好是在python中，但是我的Googlefu让我失败了。程序的通用验证版本如下：

from sklearn.feature_extraction.text import CountVectorizer
import os

textfiles = []
newgrams = set()
ngrams = []
hitlist = []
path = 'path of folder of textfiles'
names = ['john james doe', 'jane jill doe']

vectorizer = CountVectorizer(input = 'filename', ngram_range = (3,3), 
                         strip_accents='unicode', stop_words='english', 
                         token_pattern='[a-zA-Z\-]\\w*', 
                         encoding='utf-8', decode_error = 'replace', lowercase = True)
ngramer = vectorizer.build_analyzer()

for dirpath, dirnames, filenames in os.walk(path):
    for files in filenames:
        if files.endswith('.txt'):
            textfiles.append(files)

ctFiles = len(textfiles)
ctNames = len(names)

for i in range(ctFiles):
    newgrams = set(ngramer(path+'/'+textfiles[i]))
    ngrams.append(newgrams)

for i in range(ctNames):
    splitname = names[i].split()
    for j in range(ctFiles):
        tempset = set()
        for k in range(len(splitname)):
            if k == 0:
            ## subset only the trigrams that "match" first name
            for trigram in ngrams[j]:
                    for word in trigram.split():
                        if lev(splitname[k], word) < 2:
                            tempset.add(trigram)
            else:
            ## search that subset for middle/last name
            if len(tempset) > 0:
                    for trigram in tempset:
                        for word in trigram.split():
                            if lev(splitname[k], word) < 2:
                                hitlist.append([names[i], textfiles[j], trigram])
print(hitlist) ## eventually save to CSV

Answer 1

我正在使用fuzzyfuzzy，它在我的数据集（100K个句子）https://github.com/seatgeek/fuzzywuzzy上相当快