pandas：根據另一列中的值計算每一行的jaccard相似度

Question

我有一個 dataframe 如下，只有更多的行：

import pandas as pd

data = {'First':  ['First value', 'Second value','Third value'],
'Second': [['old','new','gold','door'], ['old','view','bold','door'],['new','view','world','window']]}

df = pd.DataFrame (data, columns = ['First','Second'])

為了計算 Jaccard 相似度，我在網上找到了這篇文章（不是我的解決方案）：

def lexical_overlap(doc1, doc2): 
    words_doc1 = set(doc1) 
    words_doc2 = set(doc2)

    intersection = words_doc1.intersection(words_doc2)
    union = words_doc1.union(words_doc2)
    
    return float(len(intersection)) / len(union) * 100

因此，我想要得到的結果是度量將第二列的每一行作為 doc，並迭代地比較每一對並輸出具有第一列中行名稱的度量，如下所示：

First value and Second value = 80 

First value and Third value  = 95

Second value and Third value = 90

Answer 1

由於您的數據不大，您可以嘗試使用稍微不同的方法進行廣播：

# dummy for each rows
s = pd.get_dummies(df.Second.explode()).sum(level=0).values

# pair-wise jaccard
(s@s.T)/(s|s[:,None,:]).sum(-1) * 100

Output：

array([[100.        ,  33.33333333,  14.28571429],
       [ 33.33333333, 100.        ,  14.28571429],
       [ 14.28571429,  14.28571429, 100.        ]])

Answer 2

好吧，我會這樣做：

from itertools import combinations

for val in list(combinations(range(len(df)), 2)):
    firstlist = df.iloc[val[0],1]
    secondlist = df.iloc[val[1],1]
    
    value = round(lexical_overlap(firstlist,secondlist),2)
    
    print(f"{df.iloc[val[0],0]} and {df.iloc[val[1],0]}'s value is: {value}")

Output：

First value and Second value's value is: 33.33
First value and Third value's value is: 14.29
Second value and Third value's value is: 14.29