浅谈Python 敏感词过滤的实现 - python基础

python绝技：运用python成为顶级黑客中文pdf完整版[42MB]

一个简单的实现

class NaiveFilter():

  '''Filter Messages from keywords

  very simple filter implementation

  >>> f = NaiveFilter()
  >>> f.add("sexy")
  >>> f.filter("hello sexy baby")
  hello **** baby
  '''

  def __init__(self):
    self.keywords = set([])

  def parse(self, path):
    for keyword in open(path):
      self.keywords.add(keyword.strip().decode('utf-8').lower())

  def filter(self, message, repl="*"):
    message = str(message).lower()
    for kw in self.keywords:
      message = message.replace(kw, repl)
    return message

其中strip() 函数删除附近的一些空格，解码采用utf-8的形式，然后将其转为小写。

parse()函数就是打开文件，然后从中取各个关键词，然后将其存在关键词集合中。

filter()函数是一个过滤器函数，其中将消息转化为小写，然后将关键词替换成*。、

class BSFilter:

  '''Filter Messages from keywords

  Use Back Sorted Mapping to reduce replacement times

  >>> f = BSFilter()
  >>> f.add("sexy")
  >>> f.filter("hello sexy baby")
  hello **** baby
  '''

  def __init__(self):
    self.keywords = []
    self.kwsets = set([])
    self.bsdict = defaultdict(set)
    self.pat_en = re.compile(r'^[0-9a-zA-Z]+$') # english phrase or not

  def add(self, keyword):
    if not isinstance(keyword, str):
      keyword = keyword.decode('utf-8')
    keyword = keyword.lower()
    if keyword not in self.kwsets:
      self.keywords.append(keyword)
      self.kwsets.add(keyword)
      index = len(self.keywords) - 1
      for word in keyword.split():
        if self.pat_en.search(word):
          self.bsdict[word].add(index)
        else:
          for char in word:
            self.bsdict[char].add(index)

  def parse(self, path):
    with open(path, "r") as f:
      for keyword in f:
        self.add(keyword.strip())

  def filter(self, message, repl="*"):
    if not isinstance(message, str):
      message = message.decode('utf-8')
    message = message.lower()
    for word in message.split():
      if self.pat_en.search(word):
        for index in self.bsdict[word]:
          message = message.replace(self.keywords[index], repl)
      else:
        for char in word:
          for index in self.bsdict[char]:
            message = message.replace(self.keywords[index], repl)
    return message

在上面的实现例子中，对于搜索查找进行了优化，对于英语单词，直接进行了按词索引字典查找。对于其他语言模式，我们采用逐字符查找匹配的一种模式。

BFS：宽度优先搜索方式。

class DFAFilter():

  '''Filter Messages from keywords

  Use DFA to keep algorithm perform constantly

  >>> f = DFAFilter()
  >>> f.add("sexy")
  >>> f.filter("hello sexy baby")
  hello **** baby
  '''

  def __init__(self):
    self.keyword_chains = {}
    self.delimit = '\x00'

  def add(self, keyword):
    if not isinstance(keyword, str):
      keyword = keyword.decode('utf-8')
    keyword = keyword.lower()
    chars = keyword.strip()
    if not chars:
      return
    level = self.keyword_chains
    for i in range(len(chars)):
      if chars[i] in level:
        level = level[chars[i]]
      else:
        if not isinstance(level, dict):
          break
        for j in range(i, len(chars)):
          level[chars[j]] = {}
          last_level, last_char = level, chars[j]
          level = level[chars[j]]
        last_level[last_char] = {self.delimit: 0}
        break
    if i == len(chars) - 1:
      level[self.delimit] = 0

  def parse(self, path):
    with open(path,encoding='UTF-8') as f:
      for keyword in f:
        self.add(keyword.strip())

  def filter(self, message, repl="*"):
    if not isinstance(message, str):
      message = message.decode('utf-8')
    message = message.lower()
    ret = []
    start = 0
    while start < len(message):
      level = self.keyword_chains
      step_ins = 0
      for char in message[start:]:
        if char in level:
          step_ins += 1
          if self.delimit not in level[char]:
            level = level[char]
          else:
            ret.append(repl * step_ins)
            start += step_ins - 1
            break
        else:
          ret.append(message[start])
          break
      else:
        ret.append(message[start])
      start += 1

    return ''.join(ret)

DFA即Deterministic Finite Automaton，也就是确定有穷自动机。

使用了嵌套的字典来实现。

参考

Github:敏感词过滤系统

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持谷谷点程序。

转载请注明：谷谷点程序 » 浅谈Python 敏感词过滤的实现