大家好，又见面了，我是你们的朋友全栈君。

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