如何从pandas数据帧计算jaccard相似性

使用scikit的成对距离’汉明’学习：

from sklearn.metrics.pairwise import pairwise_distances
jac_sim = 1 - pairwise_distances(df.T,metric = "hamming")
# optionally convert it to a DataFrame
jac_sim = pd.DataFrame(jac_sim,index=df.columns,columns=df.columns)

说明：

假设这是您的数据集：

import pandas as pd
import numpy as np
np.random.seed(0)
df = pd.DataFrame(np.random.binomial(1,0.5,size=(100,5)),columns=list('ABCDE'))
print(df.head())

   A  B  C  D  E
0  1  1  1  1  0
1  1  0  1  1  0
2  1  1  1  1  0
3  0  0  1  1  1
4  1  1  0  1  0

使用sklearn的jaccard_similarity_score,A列和B列之间的相似性为：

from sklearn.metrics import jaccard_similarity_score
print(jaccard_similarity_score(df['A'],df['B']))
0.43

这是与总行数100相同的值的行数.

据我所知,没有成对版本的jaccard_similarity_score,但有成对版本的距离.

但是,SciPy将Jaccard distance定义如下：

Given two vectors,u and v,the Jaccard distance is the proportion of those elements u[i] and v[i] that disagree where at least one of them is non-zero.

因此它排除了两列都有0值的行. jaccard_similarity_score没有.另一方面,汉明距离与相似性定义一致：

The proportion of those vector elements between two n-vectors u and v
which disagree.

所以如果你想计算jaccard_similarity_score,你可以使用1 – 汉明：

from sklearn.metrics.pairwise import pairwise_distances
print(1 - pairwise_distances(df.T,metric = "hamming"))

array([[ 1.,0.43,0.61,0.55,0.46],[ 0.43,1.,0.52,0.56,0.49],[ 0.61,0.48,0.53],[ 0.55,[ 0.46,0.49,0.53,1.  ]])

在DataFrame格式中：

jac_sim = 1 - pairwise_distances(df.T,metric = "hamming")
jac_sim = pd.DataFrame(jac_sim,columns=df.columns)
# jac_sim = np.triu(jac_sim) to set the lower diagonal to zero
# jac_sim = np.tril(jac_sim) to set the upper diagonal to zero

      A     B     C     D     E
A  1.00  0.43  0.61  0.55  0.46
B  0.43  1.00  0.52  0.56  0.49
C  0.61  0.52  1.00  0.48  0.53
D  0.55  0.56  0.48  1.00  0.49
E  0.46  0.49  0.53  0.49  1.00

您可以通过迭代列的组合来执行相同操作,但速度会慢得多.

import itertools
sim_df = pd.DataFrame(np.ones((5,columns=df.columns)
for col_pair in itertools.combinations(df.columns,2):
    sim_df.loc[col_pair] = sim_df.loc[tuple(reversed(col_pair))] = jaccard_similarity_score(df[col_pair[0]],df[col_pair[1]])
print(sim_df)
      A     B     C     D     E
A  1.00  0.43  0.61  0.55  0.46
B  0.43  1.00  0.52  0.56  0.49
C  0.61  0.52  1.00  0.48  0.53
D  0.55  0.56  0.48  1.00  0.49
E  0.46  0.49  0.53  0.49  1.00