python-voi 0.0.1

Python implementation of Variation of Information

pyvoi: Variation of Information in numpy/torch

Calculate the Variation of Information for two clusterings

References: Meila, Marina (2003). Comparing Clusterings by the Variation of Information. Learning Theory and Kernel Machines: 173–187

Installation

pip install pyvoi

Usage

By default, pyvoi uses torch to compute the variation of information.

To get VI values only

import pyvoi
labels1=[0,1,1,2,4]
labels2=[0,2,3,4,4]
vi,vi_split,vi_merge=pyvoi.VI(labels1,labels2)
print(vi,vi_split,vi_merge)
# total vi, split vi, merge vi
# tensor(0.5545) tensor(0.2773) tensor(0.2773)

To get VI components

import pyvoi
labels1=[0,1,1,2,4]
labels2=[0,2,3,4,4]
vi,vi_split,vi_merge,splitters,mergers=pyvoi.VI(labels1,labels2,return_split_merge=True)
print(vi,vi_split,vi_merge)
# (same as above) total vi, split vi, merge vi
# tensor(0.5545) tensor(0.2773) tensor(0.2773)

print(splitters)
# vi contribution, label in label2
#tensor([[0.2773, 4.0000],
#    [0.0000, 3.0000],
#    [0.0000, 2.0000],
#    [0.0000, 0.0000]])

print(mergers)
# vi contribution, label in label1
#tensor([[0.2773, 1.0000],
#    [0.0000, 4.0000],
#    [0.0000, 2.0000],
#    [0.0000, 0.0000]])

Using cuda

import pyvoi
labels1=[0,1,1,2,4]
labels2=[0,2,3,4,4]
vi,vi_split,vi_merge=pyvoi.VI(labels1,labels2,device="cuda")
print(vi,vi_split,vi_merge)
#tensor(0.5545, device='cuda:0') tensor(0.2773, device='cuda:0') tensor(0.2773, device='cuda:0')

Using numpy

import numpy as np
import pyvoi
labels1=np.array([0,1,1,2,4])
labels2=np.array([0,2,3,4,4])
vi,vi_split,vi_merge=pyvoi.VI(labels1,labels2,torch=False)
print(vi,vi_split,vi_merge)
#0.5545177444479561 0.27725887222397794 0.27725887222397816

Contact and Bug Reports

Core Francisco Park: [e-mail]