如何将列表中的每个元素与另一个列表中的每个元素进行比较?

how to compare each element in a list with each element in another list?

我想将提取的促销代码列表与正确的促销代码列表进行比较。

如果 extracted_list 中的促销代码正在与 correct_promo_code 列表中的促销代码进行比较,则表示促销代码有错误。为了从 correct_promo_codes 列表中找到正确的促销代码,我需要找到与被比较的促销代码(来自 extracted_list)具有最小编辑距离(levenshtein 距离)的促销代码。

代码到现在:-

import csv

with open("all_correct_promo.csv","rb") as file1:
    reader1 = csv.reader(file1)
    correctPromoList = list(reader1)
    #print correctPromoList

with open("all_extracted_promo.csv","rb") as file2:
    reader2 = csv.reader(file2)
    extractedPromoList = list(reader2)
    #print extractedPromoList

incorrectPromo = []
count = 0
for extracted in extractedPromoList:
    if(extracted not in correctPromoList):
        incorrectPromo.append(extracted)
    else:
        count = count + 1
#print incorrectPromo

for promos in incorrectPromo:
    print promos

根据nltk docs

nltk.metrics.distance.edit_distance(s1, s2, transpositions=False)

计算两个字符串之间的 Levenshtein 编辑距离。编辑距离是将 s1 转换为 s2 需要替换、插入或删除的字符数。例如,将“rain”转换为“shine”需要三个步骤,包括两次替换和一次插入:“rain”->“sain”->“shin”->“shine”。这些操作本来可以在其他顺序中完成,但至少需要三个步骤。

来到你的代码,我认为下半部分的一些变化将捕获编辑距离 -

from nltk.metrics import distance # slow to load

extractedPromoList = ['abc','acd','abd'] # csv of extracted promo codes dummy
correctPromoList = ['abc','aba','xbz','abz','abx'] # csv to real promo codes dummy

def find_min_edit(str_,list_):
    nearest_correct_promos = []
    distances = {}
    min_dist = 100 # arbitrary large assignment
    for correct_promo in list_:
        dist = distance.edit_distance(extracted,correct_promo,True) # compute Levenshtein distance
        distances[correct_promo] = dist # store each score for real promo codes
        if dist<min_dist:
            min_dist = dist # store min distance
    # extract all real promo codes with minimum Levenshtein distance
    nearest_correct_promos.append(','.join([i[0] for i in distances.items() if i[1]==min_dist])) 
    return ','.join(nearest_correct_promos) # return a comma separated string of nearest real promo codes

incorrectPromo = {}
count = 0
for extracted in extractedPromoList:
    print 'Computing %dth promo code...' % count
    incorrectPromo[extracted] =  find_min_edit(extracted,correctPromoList) # get comma separated str of real promo codes nearest to extracted
    count+=1
print incorrectPromo

输出

Computing 0th promo code...
Computing 1th promo code...
Computing 2th promo code...
{'abc': 'abc', 'abd': 'abx,aba,abz,abc', 'acd': 'abx,aba,abz,abc'}