pandas 使用jaccard相似度的Python Pandas距离矩阵
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/35639571/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Python Pandas Distance matrix using jaccard similarity
提问by J-H
I have implemented a function to construct a distance matrix using the jaccard similarity:
我已经实现了一个使用 jaccard 相似度构造距离矩阵的函数：
import pandas as pd
entries = [
{'id':'1', 'category1':'100', 'category2': '0', 'category3':'100'},
{'id':'2', 'category1':'100', 'category2': '0', 'category3':'100'},
{'id':'3', 'category1':'0', 'category2': '100', 'category3':'100'},
{'id':'4', 'category1':'100', 'category2': '100', 'category3':'100'},
{'id':'5', 'category1':'100', 'category2': '0', 'category3':'100'}
]
df = pd.DataFrame(entries)
and the distance matrix with scipy
和 scipy 的距离矩阵
from scipy.spatial.distance import squareform
from scipy.spatial.distance import pdist, jaccard
res = pdist(df[['category1','category2','category3']], 'jaccard')
squareform(res)
distance = pd.DataFrame(squareform(res), index=df.index, columns= df.index)
The problem is that my result looks like this which seems to be false:
问题是我的结果看起来像这样，这似乎是错误的：
What am i missing? The similarity of 0 and 1 have to be maximum for example and the other values seem wrong too
我错过了什么？例如，0 和 1 的相似度必须是最大的，其他值似乎也是错误的
采纳答案by root
Looking at the docs, the implementation of jaccardin scipy.spatial.distanceis jaccard dissimilarity, not similarity. This is the usual way in which distance is computed when using jaccard as a metric. The reason for this is because in order to be a metric, the distance between the identical points must be zero.
查看文档，jaccardin的实现scipy.spatial.distance是 jaccard dissimilarity，而不是相似性。这是使用 jaccard 作为度量时计算距离的常用方法。这样做的原因是，为了成为度量，相同点之间的距离必须为零。
In your code, the dissimilarity between 0 and 1 should be minimized, which it is. The other values look correct in the context of dissimilarity as well.
在您的代码中，应该最小化 0 和 1 之间的差异，事实就是如此。其他值在不同的上下文中看起来也是正确的。
If you want similarity instead of dissimilarity, just subtract the dissimilarity from 1.
如果你想要相似而不是相异，只需从 1 中减去相异。
res = 1 - pdist(df[['category1','category2','category3']], 'jaccard')
