算法-DFA算法-敏感词过滤算法(OC、Swift、Python)「建议收藏」

前言前段时间,公司的IMSDK想做敏感词过滤,但是后端的小伙伴《比较忙》,在开产品需求会的时候想把敏感词过滤放到前端,让iOS、安卓自己搞,但是前端小伙伴写了一个方法来检测一段文本,耗时一两秒钟而且比较耗CPU,这样肯定不行的,最后后端小伙伴妥协了,把敏感词过滤放到后端了。一般的思路可能是遍历敏感词库,然后把一段文字的敏感词过滤掉,但是针对比较大的词库时(比如我们的敏感词库10万),这样非…

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前言

前段时间,公司的IM SDK想做敏感词过滤,但是后端的小伙伴《比较忙》,在开产品需求会的时候想把敏感词过滤放到前端,让iOS、安卓自己搞,但是前端小伙伴写了一个方法来检测一段文本,耗时【一两秒】钟而且比较耗CPU,这样肯定不行的,最后后端小伙伴妥协了,把敏感词过滤放到后端了。

一般的思路可能是遍历敏感词库,然后把一段文字的敏感词过滤掉,但是针对比较大的词库时(比如我们的敏感词库10万),这样非常耗时和耗内存,在电脑上还能跑跑,但是在手机上分分钟钟被系统杀死掉,这样肯定是不行的,这里就用到一种DFA算法。

但是使用了DFA算法,十万的敏感词库过滤一句话只需要【0.434510秒】!

2019-10-23 14:34:08.230918+0800 DFAFilterDemo[4728:4650502] message == 小明骂小王是个王八蛋,小王骂小明是个王八羔子!
2019-10-23 14:34:08.232972+0800 DFAFilterDemo[4728:4650502] result == 小明骂小王是个***,小王骂小明是个王八羔子!
2019-10-23 14:34:08.316380+0800 DFAFilterDemo[4728:4650502] 总共耗时: 0.434510 

DFA算法

简介

何谓DFA,它的全称是Deterministic Finite Automaton,即确定有穷自动机;其特征为:有一个有限状态集合和一些从一个状态通向另一个状态的边,每条边上标记有一个符号,其中一个状态是初态,某些状态是终态。但不同于不确定的有限自动机,DFA中不会有从同一状态出发的两条边标志有相同的符号;DFA算法的核心是建立了以敏感词为基础的许多敏感词树。

描述

我们先把敏感词库拆分解析成一个”敏感词树“,我们以敏感词”王八蛋“和”王八羔子“为例:
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拆成的敏感词树如下:
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代码

OC代码

//
// DFAFilter.m
// DFAFilterDemo
//
// Created by 张福杰 on 2019/10/22.
// Copyright © 2019 张福杰. All rights reserved.
//

#import "DFAFilter.h"

@interface DFAFilter ()

@property (nonatomic,strong) NSMutableDictionary *keyword_chains;
@property (nonatomic,  copy) NSString *delimit;

@end

@implementation DFAFilter

- (instancetype)init{ 
   
    if(self == [super init]){ 
   
        _delimit = @"\x00";
    }
    return self;
}

/// 读取解析敏感词
- (void)parseSensitiveWords:(NSString *)path{ 
   
    if(path == nil){ 
   
        path = [[NSBundle mainBundle] pathForResource:@"sensitive_words" ofType:@"txt"];
    }
    NSString *content = [[NSString alloc] initWithContentsOfFile:path encoding:NSUTF8StringEncoding error:nil];
    NSArray *keyWordList = [content componentsSeparatedByString:@","];
    for (NSString *keyword in keyWordList) { 
   
        [self addSensitiveWords:keyword];
    }
    
    NSLog(@"keyword_chains == %@",self.keyword_chains);
}

- (void)addSensitiveWords:(NSString *)keyword{ 
   
    keyword = keyword.lowercaseString;
    keyword = [keyword stringByTrimmingCharactersInSet:[NSCharacterSet whitespaceAndNewlineCharacterSet]];
    
    NSMutableDictionary *node = self.keyword_chains;
    for (int i = 0; i < keyword.length; i ++) { 
   
        NSString *word = [keyword substringWithRange:NSMakeRange(i, 1)];
        if (node[word] == nil) { 
   
            node[word] = [NSMutableDictionary dictionary];
        }
        node = node[word];
    }
    //敏感词最后一个字符标识
    [node setValue:@0 forKey:self.delimit];
}

- (NSString *)filterSensitiveWords:(NSString *)message replaceKey:(NSString *)replaceKey{ 
   
    replaceKey = replaceKey == nil ? @"*" : replaceKey;
    message = message.lowercaseString;
    NSMutableArray *retArray = [[NSMutableArray alloc] init];
    NSInteger start = 0;
    while (start < message.length) { 
   
        NSMutableDictionary *level = self.keyword_chains.mutableCopy;
        NSInteger step_ins = 0;
        NSString *message_chars = [message substringWithRange:NSMakeRange(start, message.length - start)];
        for(int i = 0; i < message_chars.length; i++){ 
   
            NSString *chars_i = [message_chars substringWithRange:NSMakeRange(i, 1)];
            if([level.allKeys containsObject:chars_i]){ 
   
                step_ins += 1;
                NSDictionary *level_char_dict = level[chars_i];
                if(![level_char_dict.allKeys containsObject:self.delimit]){ 
   
                    level = level_char_dict.mutableCopy;
                }else{ 
   
                    NSMutableString *ret_str = [[NSMutableString alloc] init];
                    for(int i = 0; i < step_ins; i++){ 
   
                         [ret_str appendString:replaceKey];
                    }
                    [retArray addObject:ret_str];
                    start += (step_ins - 1);
                    break;
                }
            }else{ 
   
                [retArray addObject:[NSString stringWithFormat:@"%C",[message characterAtIndex:start]]];
                break;
            }
        }
        start ++;
    }
    return [retArray componentsJoinedByString:@""];
}

- (NSMutableDictionary *)keyword_chains{ 
   
    if(_keyword_chains == nil){ 
   
        _keyword_chains = [[NSMutableDictionary alloc] initWithDictionary:@{ 
   }];
    }
    return _keyword_chains;
}

@end

Swift代码

//
// DFAFilter.swift
// DFAFilterDemo
//
// Created by 张福杰 on 2019/10/23.
// Copyright © 2019 张福杰. All rights reserved.
//

import UIKit

class DFAFilter: NSObject { 
   
    lazy var keyword_chains: NSMutableDictionary = { 
   
        let dict = NSMutableDictionary()
        return dict
    }()
    
    lazy var delimit: String = "\\x00";
    
    /// 读取敏感词
    func parseSensitiveWords() -> Void { 
   
        let path = Bundle.main.path(forResource: "sensitive_words", ofType: "txt");
        let url = URL(fileURLWithPath: path!)
        do { 
   
            let data = try Data(contentsOf: url)
            let content: String = String(data: data, encoding: String.Encoding.utf8)!
            let keyWordList = content.components(separatedBy: ",")
            for keyword in keyWordList { 
   
                addSensitiveWords(keyword)
            }
            
        } catch let error as Error? { 
   
            print(error?.localizedDescription as Any)
        }
    }
    
    /// 添加敏感词到敏感词树
    func addSensitiveWords(_ keyword: String) -> Void { 
   
        let keyword: String = keyword.lowercased().trimmingCharacters(in: .whitespacesAndNewlines)
        var node = self.keyword_chains
        if keyword.count <= 0{ 
   
            return
        }
        
        for index in 0...keyword.count - 1 { 
   
            let index0 = keyword.index(keyword.startIndex, offsetBy: index)
            let index1 = keyword.index(keyword.startIndex, offsetBy: index + 1)
            let word = keyword[index0..<index1]
            if node[word] == nil{ 
   
                node[word] = NSMutableDictionary()
            }
            node = node[word] as! NSMutableDictionary
        }
        node[self.delimit] = 0
    }
    
    /// 开始过滤敏感词
    func filterSensitiveWords(_ message: String, replaceKey: String) -> String { 
   
        let replaceKey = replaceKey.count > 0 ? replaceKey : "*"
        let message = message.lowercased()
        let retArray: NSMutableArray = NSMutableArray()
        var start = 0
        while start < message.count { 
   
            var level: NSMutableDictionary = self.keyword_chains.mutableCopy() as! NSMutableDictionary
            var step_ins = 0
            let message_chars = getChar(message, startIndex: start, endIndex: message.count)
            for index in 0...message_chars.count - 1 { 
   
                let chars_i = getChar(message_chars, startIndex: index, endIndex: index + 1)
                if level[chars_i] != nil{ 
   
                    step_ins += 1
                    let level_char_dict: NSDictionary = level[chars_i] as! NSDictionary
                    if level_char_dict[self.delimit] == nil{ 
   
                        level = level_char_dict.mutableCopy() as! NSMutableDictionary
                    }else{ 
   
                        var ret_str = ""
                        for _ in 0...step_ins - 1 { 
   
                            ret_str += replaceKey
                        }
                        retArray.add(ret_str)
                        start += (step_ins - 1)
                        break
                    }
                }else{ 
   
                    let word = getChar(message, startIndex: start, endIndex: start + 1)
                    retArray.add(word)
                    break
                }
            }
            start += 1
        }

        return retArray.componentsJoined(by: "")
    }
    
    func getChar(_ message: String, startIndex: NSInteger, endIndex: NSInteger) -> String { 
   
        let index0 = message.index(message.startIndex, offsetBy: startIndex)
        let index1 = message.index(message.startIndex, offsetBy: endIndex)
        let word = message[index0..<index1]
        return String(word)
    }

}

Python代码

# -*- coding: utf-8 -*-
# @Author: zhangfujie
# @Date: 2019/10/22
# @Last Modified by: zhangfujie
# @Last Modified time: 2019/10/22
# @ ---------- DFA过滤算 ---------- 
import time
time1 = time.time()

class DFAFilter(object):
	"""DFA过滤算法"""
	def __init__(self):
		super(DFAFilter, self).__init__()
		self.keyword_chains = { 
   }
		self.delimit = '\x00'

	# 读取解析敏感词
	def parseSensitiveWords(self, path):
		ropen = open(path,'r')
		text = ropen.read()
		keyWordList = text.split(',')
		for keyword in keyWordList:
			self.addSensitiveWords(str(keyword).strip())

	# 生成敏感词树
	def addSensitiveWords(self, keyword):
		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

	# 过滤敏感词
	def filterSensitiveWords(self, message, repl="*"):
		message = message.lower()
		ret = []
		start = 0
		while start < len(message):
			level = self.keyword_chains
			step_ins = 0
			message_chars = message[start:]
			for char in message_chars:
				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
			start += 1

		return ''.join(ret)


if __name__ == "__main__":
	gfw = DFAFilter()
	gfw.parseSensitiveWords('shieldwords.txt')
	text = "小明骂小王是个王八蛋,小王骂小明是个王八羔子!"
	result = gfw.filterSensitiveWords(text)
	print(result)
	time2 = time.time()
	print('总共耗时:' + str(time2 - time1) + 's')	

结束语

demo下载地址: https://gitee.com/zfj1128/DFAFilterDemo
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