python相似性检测的安装包

安装python-Levenshtein模块

pip install python-Levenshtein

使用python-Levenshtein模块

import Levenshtein

算法说明

1). Levenshtein.hamming(str1, str2)
计算汉明距离。要求str1和str2必须长度一致。是描述两个等长字串之间对应 位置上不同字符的个数。

2). Levenshtein.distance(str1, str2)
计算编辑距离（也称为 Levenshtein距离）。是描述由一个字串转化成另一个字串最少的操作次数，在其中的操作包括插入、删除、替换。
算法实现参考动态规划整理。

3). Levenshtein.ratio(str1, str2)
计算莱文斯坦比。计算公式r = (sum - ldist) / sum, 其中sum是指str1 和 str2 字串的长度总和，ldist是 类编辑距离
注意 ：这里的类编辑距离不是2中所说的编辑距离，2中三种操作中每个操作+1，而在此处，删除、插入依然+1，但是替换+2
这样设计的目的：ratio('a', 'c')，sum=2, 按2中计算为（2-1）/2 = 0.5,’a','c'没有重合，显然不合算，但是替换操作+2，就可以解决这个问题。

4). Levenshtein.jaro(s1 , s2 )
计算jaro距离，

其中的 m 为s1 , s2的匹配长度，当某位置的认为匹配当该位置字符相同，或者在不超过

t是调换次数的一半

5.) Levenshtein.jaro_winkler(s 1 , s 2 )
计算 Jaro–Winkler距离：

import Levenshtein 报错：ImportError: No module named Levenshtein

于是去： python-Levenshtein 下载源码进行安装（在 http://www.lfd.uci.edu/~gohlke/pythonlibs/#python-levenshtein其实也有编译好的exe），第一次安装的时候报错：error: Unable to find vcvarsall.bat ，但其实我是装了VS2010的，所以执行如下步骤正常安装：

1.设置环境变量，执行：

SET VS90COMNTOOLS=%VS100COMNTOOLS%

2.再去安装：

setup.py install

就可以正常，编译，安装了。

$ python
>>> import Levenshtein
>>> help(Levenshtein.ratio)
ratio(...)
    Compute similarity of two strings.

    ratio(string1, string2)

    The similarity is a number between 0 and 1, it's usually equal or
    somewhat higher than difflib.SequenceMatcher.ratio(), becuase it's
    based on real minimal edit distance.

    Examples:
    >>> ratio('Hello world!', 'Holly grail!')
    0.58333333333333337
    >>> ratio('Brian', 'Jesus')
    0.0

>>> help(Levenshtein.distance)
distance(...)
    Compute absolute Levenshtein distance of two strings.

    distance(string1, string2)

    Examples (it's hard to spell Levenshtein correctly):
    >>> distance('Levenshtein', 'Lenvinsten')
    4
    >>> distance('Levenshtein', 'Levensthein')
    2
    >>> distance('Levenshtein', 'Levenshten')
    1
    >>> distance('Levenshtein', 'Levenshtein')
    0

• difflib 库

• >>> import difflib
  
  >>> difflib.SequenceMatcher(None, 'abcde', 'abcde').ratio()
  1.0
  
  >>> difflib.SequenceMatcher(None, 'abcde', 'zbcde').ratio()
  0.80000000000000004
  
  >>> difflib.SequenceMatcher(None, 'abcde', 'zyzzy').ratio()
  0.0

•  

  FuzzyWuzzy

  git clone git://github.com/seatgeek/fuzzywuzzy.git fuzzywuzzy
  cd fuzzywuzzy
  python setup.py install
  
  >>> from fuzzywuzzy import fuzz
  >>> from fuzzywuzzy import process
  
  Simple Ratio
  >>> fuzz.ratio("this is a test", "this is a test!")
      96
  
  Partial Ratio
  >>> fuzz.partial_ratio("this is a test", "this is a test!")
      100
  
  Token Sort Ratio
  >>> fuzz.ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
      90
  >>> fuzz.token_sort_ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
      100
  
  Token Set Ratio
  >>> fuzz.token_sort_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
      84
  >>> fuzz.token_set_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
      100

  gitclone git://github.com/seatgeek/fuzzywuzzy.git fuzzywuzzy
  cdfuzzywuzzy
  pythonsetup.pyinstall
   
  >>> fromfuzzywuzzyimportfuzz
  >>> fromfuzzywuzzyimportprocess
   
  SimpleRatio
  >>> fuzz.ratio("this is a test", "this is a test!")
      96
   
  PartialRatio
  >>> fuzz.partial_ratio("this is a test", "this is a test!")
      100
   
  TokenSortRatio
  >>> fuzz.ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
      90
  >>> fuzz.token_sort_ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
      100
   
  TokenSetRatio
  >>> fuzz.token_sort_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
      84
  >>> fuzz.token_set_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
      100
  

  google-diff-match-patch

  import diff match patch textA = "the cat in the red hat" textB = "the feline in the blue hat"

  dmp = diff match patch.diff match patch()  #create a diff match patch object diffs = dmp.diff main(textA, textB)   # All 'diff' jobs start with invoking diff main()

  d value = dmp.diff levenshtein(diffs) print d_value

  maxLenth = max(len(textA), len(textB)) print float(d_value)/float(maxLenth)

  similarity = (1 - float(d_value)/float(maxLenth)) * 100 print similarity

  importdiff_match_patch
  textA = "the cat in the red hat"
  textB = "the feline in the blue hat"
   
  dmp = diff_match_patch.diff_match_patch()  #create a diff_match_patch object
  diffs = dmp.diff_main(textA, textB)   # All 'diff' jobs start with invoking diff_main()
   
  d_value = dmp.diff_levenshtein(diffs)
  printd_value
   
  maxLenth = max(len(textA), len(textB))
  printfloat(d_value)/float(maxLenth)
   
  similarity = (1 - float(d_value)/float(maxLenth)) * 100
  printsimilarity

• title2

第二种方法安装

参考博客：http://blog.csdn.net/TH_NUM/article/details/77095177

安装pip install python-Levenshtein,出现错误：Microsoft Visual C++ 14.0 is required

出现错误主要是因为直接使用 pip install 【第三方库名】 安装自己需要的第三方库。

解决办法：

一定要安装和自己windows版本和python版本对应的第三方库

在这里下载需要的第三方库：http://www.lfd.uci.edu/~gohlke/pythonlibs

安装步骤：

首先：在网站上下载对于版本的 python_Levenshtein-0.12.0-cp36-cp36m-win_amd64.whl

然后：然后在控制台上切换到放置位置后输入pip install python_Levenshtein-0.12.0-cp36-cp36m-win_amd64.whl

就完成了！！！