本文主要介绍了Python 敏感词过滤的实现示例，文中通过示例代码介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们可以参考一下
一个简单的实现
主要是通过循环和replace的方式进行敏感词的替换
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(宽度优先搜索)进行实现
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 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)进行实现
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。
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 到此这篇关于Python 敏感词过滤的实现示例的文章就介绍到这了,更多相关Python 敏感词过滤内容请搜索html中文网以前的文章或继续浏览下面的相关文章希望大家以后多多支持html中文网！
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