Python Pandas Distance matrix using jaccard similarity

Question

I have implemented a function to construct a distance matrix using the jaccard similarity:

import pandas as pd
entries = [
    {\'id\':\'1\', \'category1\':\         


        

Answer 1

Looking at the docs, the implementation of jaccard in scipy.spatial.distance is 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.

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.

If you want similarity instead of dissimilarity, just subtract the dissimilarity from 1.

res = 1 - pdist(df[['category1','category2','category3']], 'jaccard')