浅谈Python 敏感词过滤的实现

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

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