如何找到熊猫中班级的杰卡德相似性

Question

我想找到数据集中每对组之间的Jaccard相似性。我的数据如下，第一列是我的数据，第二列是类标签：

import pandas as pd
import numpy as np
df = pd.DataFrame({'Data' : ["a1","a2","a3","a4","a5","a6","a7"], 'ClassLable' :     ["c1","c2","c2","c2","c3","c3","c1"]}); df
df2 = pd.DataFrame({'Data' : ["a1","a2","a4","a6","a7","a8","a9"], 'ClassLable' : ["c11","c21","c21","c12","c13","c13","c11"]}); df2

我想计算 df 和 df2 之间每对类标签的杰卡指数。 例如：

c1class = pd.DataFrame({'Data':["a1","a7"]})
c11class = pd.DataFrame({'Data':["a1","a9"]})
Jaccard = 1/3

换句话说，对于 DF1 和 DF2，我想为每个类 lable 找到并集上的相交项

Answer 1

您是否正在寻找这样的东西：

from sklearn.metrics import jaccard_similarity_score
jaccard_similarity_score(df['Data'],df2['Data'])
Out[92]: 0.2857142857142857
jaccard_similarity_score(c1class, c11class)
Out[93]: 0.5