elisa-aleman/rbo

Python implementation of the rank-biased overlap list similarity measure.

Rank-biased overlap

Overview

A small Python module for calculating rank-biased overlap, a measure of similarity between ragged, possibly infinite ranked lists which may or may not contain the same items (up to the actually evaluated depth or at all). See "A similarity measure for indefinite rankings" by W. Webber, A. Moffat and J. Zobel (2011), http://dx.doi.org/10.1145/1852102.1852106.

The definition of overlap has been modified to account for ties. Without this, results for lists with tied items were being inflated. The modification itself is not mentioned in the paper but seems to be reasonable, see function overlap(). Places in the code which diverge from the spec in the paper because of this are highlighted with comments (search for "NOTE").

The functions intended for external use are rbo() and rbo_dict(), plus possibly average_overlap() (for comparison purposes). rbo() receives two sorted lists where each individual item is a hashable object or a set of hashable objects (to represent ties):

>>> rbo([{"a", "c"}, "b", "d"], ["a", {"b", "c"}, "d"], p=.9)
RBO(min=0.48919503099801515, res=0.47747163566865164, ext=0.9666666666666667)

The function returns a namedtuple with three fields whose values correspond to three RBO estimates (all defined in the paper):

• min is a lower-bound estimate
• res is the corresponding residual; min + res constitutes an upper bound estimate
• ext is an extrapolated point estimate

By contrast, rbo_dict takes a dict mapping the items to sort to the scores according to which they should be sorted:

>>> rbo_dict(dict(a=1, b=2, c=1, d=3), dict(a=1, b=2, c=2, d=3), p=.9, sort_ascending=True)
RBO(min=0.48919503099801515, res=0.47747163566865164, ext=0.9666666666666667)

Scores are typically the higher the better, so the sort is descending by default. You can specify sort_ascending=True to override this if you have some rank-like score (i.e. the lower the better).

Conceptually, the p parameter of both functions represents the probability that a person doing a manual comparison of the ranked lists would stop (i.e. decide she has seen enough in order to hazard a conclusion) at each transition to a lower rank. In other words, it "models the user's persistence" (Webber et. al, p. 17). Formally, it's the parameter of the geometric progression which weights the contribution of overlaps at different depths.

The code is primarily optimized for correctness, not speed. Build your own faster version and check it for correctness by comparing against this one!

Requirements

Built and tested under Python 3.5.2. No external dependencies.

License

Credits for the concept of the RBO measure are indicated above.

Copyright (this implementation) © 2016 ÚČNK/David Lukeš

This implementation is distributed under the GNU General Public License v3.