pandas:calculate 基于另一列值的每一行的 jaccard 相似度

Question

我有一个数据框如下，只有更多行：

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'])

为了计算 jaccard 相似度，我在网上找到了这篇文章（不是我的解决方案）：

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

结果我想得到的是将第二列的每一行作为文档并迭代地比较每一对并输出一个带有第一列中的行名称的度量，如下所示：

First value and Second value = 80 

First value and Third value  = 95

Second value and Third value = 90

Answer 1

由于您的数据不大，您可以尝试使用稍微不同的方法进行广播：

# 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

输出：

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

Answer 2

好吧，我会这样做：

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}")

输出：

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

pandas:calculate 基于另一列值的每一行的 jaccard 相似度

pandas:calculate jaccard similarity for every row based on the value in another column

python

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