Python 计算两个字符串(或句子)相似度的方法

主要方法有：编辑距离、余弦相似度、模糊相似度百分比

1、编辑距离

def levenshtein(first, second):
        ''' 编辑距离算法（LevD） 
            Args: 两个字符串
            returns: 两个字符串的编辑距离 int
        '''
        if len(first) > len(second):
            first, second = second, first
        if len(first) == 0:
            return len(second)
        if len(second) == 0:
            return len(first)
        first_length = len(first) + 1
        second_length = len(second) + 1
        distance_matrix = [list(range(second_length)) for x in range(first_length)]
        # print distance_matrix
        for i in range(1, first_length):
            for j in range(1, second_length):
                deletion = distance_matrix[i - 1][j] + 1
                insertion = distance_matrix[i][j - 1] + 1
                substitution = distance_matrix[i - 1][j - 1]
                if first[i - 1] != second[j - 1]:
                    substitution += 1
                distance_matrix[i][j] = min(insertion, deletion, substitution)
                # print distance_matrix
        return distance_matrix[first_length - 1][second_length - 1]
str1="hello,good moring"
str2="hi,good moring"
edit_distance=levenshtein(str1,str2)
print edit_distance

2、余弦相似度

import math
import re
import datetime
import time

text1 = "This game is one of the very best. games ive  played. the  ;pictures? cant descripe the real graphics in the game."
text2 = "this game have/ is3 one of the very best. games ive  played. the  ;pictures? cant descriPe now the real graphics in the game."


def compute_cosine(text_a, text_b):
    # 找单词及词频
    words1 = text_a.split(' ')
    words2 = text_b.split(' ')
    # print(words1)
    words1_dict = {}
    words2_dict = {}
    for word in words1:
        # word = word.strip(",.?!;")
        word = re.sub('[^a-zA-Z]', '', word)
        word = word.lower()
        # print(word)
        if word != '' and word in words1_dict: # 这里改动了
            num = words1_dict[word]
            words1_dict[word] = num + 1
        elif word != '':
            words1_dict[word] = 1
        else:
            continue
    for word in words2:
        # word = word.strip(",.?!;")
        word = re.sub('[^a-zA-Z]', '', word)
        word = word.lower()
        if word != '' and word in words2_dict:
            num = words2_dict[word]
            words2_dict[word] = num + 1
        elif word != '':
            words2_dict[word] = 1
        else:
            continue
    print(words1_dict)
    print(words2_dict)

    # 排序
    dic1 = sorted(words1_dict.items(), key=lambda asd: asd[1], reverse=True)
    dic2 = sorted(words2_dict.items(), key=lambda asd: asd[1], reverse=True)
    print(dic1)
    print(dic2)

    # 得到词向量
    words_key = []
    for i in range(len(dic1)):
        words_key.append(dic1[i][0])  # 向数组中添加元素
    for i in range(len(dic2)):
        if dic2[i][0] in words_key:
            # print 'has_key', dic2[i][0]
            pass
        else:  # 合并
            words_key.append(dic2[i][0])
    # print(words_key)
    vect1 = []
    vect2 = []
    for word in words_key:
        if word in words1_dict:
            vect1.append(words1_dict[word])
        else:
            vect1.append(0)
        if word in words2_dict:
            vect2.append(words2_dict[word])
        else:
            vect2.append(0)
    print(vect1)
    print(vect2)

    # 计算余弦相似度
    sum = 0
    sq1 = 0
    sq2 = 0
    for i in range(len(vect1)):
        sum += vect1[i] * vect2[i]
        sq1 += pow(vect1[i], 2)
        sq2 += pow(vect2[i], 2)
    try:
        result = round(float(sum) / (math.sqrt(sq1) * math.sqrt(sq2)), 2)
    except ZeroDivisionError:
        result = 0.0
    return result


if __name__ == '__main__':
    result=compute_cosine(text1, text2)
    print(result)

3、FuzzyWuzzy

from fuzzywuzzy import fuzz
fuzz.ratio("this is a test", "this is a test!")