浅谈Python 敏感词过滤的实现

一个简单的实现classNaiveFilter():'''FilterMessagesfromkeywordsverysimplefilterimplementation>>>f=NaiveFi

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

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Python 敏感词过滤 Python 敏感词