教你如何实现Python 过滤敏感词

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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

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

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。

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