TAP-k -- Python module for Threshold Average Precision

Threshold Average Precision is a metric for evaluating IR applications that present retrieval lists with a ranking score to the end user. It is described in the following publication:

Hyrum D. Carroll, Maricel G. Kann, Sergey L. Sheetlin, and John L. Spouge: "Threshold Average Precision (TAP-k): A Measure of Retrieval Efficacy Designed for Bioinformatics" (2010). Bioinformatics 26(14):1708-1713. DOI: 10.1093/bioinformatics/btq270

This Python module is based on the authors' reference implementation written in Perl, which can be found at http://www.ncbi.nlm.nih.gov/CBBresearch/Spouge/html.ncbi/tap/.

Installation

Use pip for a system-wide installation:

pip3 install git+https://github.com/OntoGene/TAP-k.git

Or simply download the stand-alone script tapk.py.

Requirements

TAP-k runs on Python 3 (Python 3.4 or higher recommended).
No other requirements.

Usage

Installing via pip creates an executable script TAP-k to run from the command line:

TAP-k [options]

If you just downloaded the stand-alone module, use the following call:

python3 tapk.py [options]

Run TAP-k --help to see a short description of all available options.

Example call with minimal output:

$ TAP-k -i test/short.tsv -k 5 -s
EPQ (threshold at 0.5 quantile)	unweighted mean TAP
5 (0.6545522081334377)	0.5664

The input file format is the same as for the original program, which is described here. All output is written to STDOUT. The output format can be changed by specifying format strings (options -f and -Q).

The module can also be used as a library:

>>> import tapk
>>> tapk.run(["test/short.tsv"], k=5)
EPQ (threshold at 0.5 quantile)	unweighted mean TAP
5 (0.6545522081334377)	0.5664