Skip to main content

Simple library to calculate Rank-biased Overlap between two lists

Project description

Rank-biased Overlap (RBO)


This project contains a Python implementation of Rank-Biased Overlap (RBO) from: Webber, William, Alistair Moffat, and Justin Zobel. "A similarity measure for indefinite rankings." ACM Transactions on Information Systems (TOIS) 28.4 (2010): 20." (Download).


RBO compares two ranked lists, and returns a numeric value between zero and one to quantify their similarity. A RBO value of zero indicates the lists are completely different, and a RBO of one means completely identical. The terms 'different' and 'identical' require a little more clarification.

Given two ranked lists:

A = ["a", "b", "c", "d", "e"]
B = ["e", "d", "c", "b", "a"]

We can see that both of them rank 5 items ("a", "b", "c", "d" and "e"), but with completely opposite order. In this case the similarity between A and B should (and will) be 0. But here we are ranking the 5 same items, hence they are conjoint. If there is third ranked list

C = ["f", "g", "h", "i", "j"]

which ranks 5 totally different items, then if we ask for the similarity between A and C, we should expect a value of 0 as well. In such non-conjoint case, we need to be able to calculate a similarity as well.

The RBO measure can handle ranked lists with different lengths as well, with proper extrapolation. For example, the RBO between the list A and list

D = ["a", "b", "c", "d", "e", "f", "g"]

will be 1.


Installation using Pip

To install the RBO module to the current interpreter with Pip:

pip install rbo

Computing RBO

The RankingSimilarity class contains the calculation for the different flavours of RBO, with clear reference to the corresponding equations in the paper. Below shows how to compute the similarity of two ranked lists S and T:

In [1]: import rbo

In [2]: S = [1, 2, 3]; T = [1, 3, 2]

In [3]: rbo.RankingSimilarity(S, T).rbo()
Out[3]: 0.8333333333333334

Accepted datatypes are Python lists and Numpy arrays. Using Pandas series is possible using the underlying Numpy array as shown below. This restriction is necessary, because using [] on a Pandas series queries the index, which might not number items contiguously, or might even be non-numeric.

In [4]: import pandas as pd

In [5]: U = pd.Series([1, 3, 2, 4, 5, 6])

In [6]: rbo.RankingSimilarity(T, U.values).rbo()
Out[6]: 1.0


Refer to the Makefile for supplementary tasks to development, e.g., executing unit tests, or checking for proper packaging. Please let me know if there is any issue.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for rbo, version 0.1.2
Filename, size File type Python version Upload date Hashes
Filename, size rbo-0.1.2-py3-none-any.whl (7.5 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size rbo-0.1.2.tar.gz (6.8 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page