| 本文主要介紹了Python 敏感詞過濾的實現示例，文中通過示例代碼介紹的非常詳細，具有一定的參考價值，感興趣的小夥伴們可以參考一下 |
一個簡單的實現
主要是通過循環和replace的方式進行敏感詞的替換
class NaiveFilter():
'''Filter Messages from keywords
very simple filter implementation
>>> f = NaiveFilter()
>>> f.parse("filepath")
>>> 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
使用BSF(寬度優先搜索)進行實現
對於搜索查找進行了優化，對於英語單詞，直接進行了按詞索引字典查找。對於其他語言模式，我們採用逐字符查找匹配的一種模式。
BFS：寬度優先搜索方式
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
使用DFA(Deterministic Finite Automaton)進行實現
DFA即Deterministic Finite Automaton，也就是確定有窮自動機。
使用了嵌套的字典來實現。
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)
到此這篇關於Python 敏感詞過濾的實現示例的文章就介紹到這了。