https://github.com/cvangysel/pytrec_eval

pytrec_eval is an Information Retrieval evaluation tool for Python, based on the popular trec_eval.
https://github.com/cvangysel/pytrec_eval

evaluation information-retrieval

Last synced: about 2 months ago
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pytrec_eval is an Information Retrieval evaluation tool for Python, based on the popular trec_eval.

• Host: GitHub
• URL: https://github.com/cvangysel/pytrec_eval
• Owner: cvangysel
• License: mit
• Created: 2017-08-14T05:26:34.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2023-10-10T13:45:12.000Z (12 months ago)
• Last Synced: 2024-08-01T09:31:50.753Z (about 2 months ago)
• Topics: evaluation, information-retrieval
• Language: C++
• Homepage: http://ilps.science.uva.nl/
• Size: 43 KB
• Stars: 275
• Watchers: 9
• Forks: 32
• Open Issues: 31
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
pytrec_eval

===========

pytrec\_eval is a Python interface to TREC's evaluation tool, [trec\_eval](https://github.com/usnistgov/trec_eval). It is an attempt to stop the cultivation of custom implementations of Information Retrieval evaluation measures for the Python programming language.

Requirements

------------

The module was developed using Python 3.5. You need a Python distribution that comes with development headers. In addition to the default Python modules, [numpy](http://www.numpy.org) and [scipy](https://www.scipy.org) are required.

Installation

------------

Installation is simple and should be relatively painless if your Python environment is functioning correctly (see below for FAQs).

	pip install pytrec_eval

	

Examples

--------

Check out the examples that simulate the standard [trec\_eval front-end](examples/trec_eval.py) and that compute [statistical significance](examples/statistical_significance.py) between two runs.

To get a grasp of how simple the module is to use, check this out:

	import pytrec_eval

	import json

	

	qrel = {

	    'q1': {

	        'd1': 0,

	        'd2': 1,

	        'd3': 0,

	    },

	    'q2': {

	        'd2': 1,

	        'd3': 1,

	    },

	}

	

	run = {

	    'q1': {

	        'd1': 1.0,

	        'd2': 0.0,

	        'd3': 1.5,

	    },

	    'q2': {

	        'd1': 1.5,

	        'd2': 0.2,

	        'd3': 0.5,

	    }

	}

	

	evaluator = pytrec_eval.RelevanceEvaluator(

	    qrel, {'map', 'ndcg'})

	

	print(json.dumps(evaluator.evaluate(run), indent=1))

	

The above snippet will return a data structure that contains the requested evaluation measures for queries `q1` and `q2`:

	{

	    'q1': {

	        'ndcg': 0.5,

	        'map': 0.3333333333333333

	    },

	    'q2': {

	        'ndcg': 0.6934264036172708,

	        'map': 0.5833333333333333

	    }

	}

	

For more like this, see the example that uses [parametrized evaluation measures](examples/simple_cut.py).

Frequently Asked Questions

--------------------------

Since the module's initial release, no questions have been asked so frequently that they deserve a spot in this section.

Citation

--------

If you use pytrec\_eval to produce results for your scientific publication, please refer to our SIGIR paper:

```

@inproceedings{VanGysel2018pytreceval,

  title={Pytrec\_eval: An Extremely Fast Python Interface to trec\_eval},

  author={Van Gysel, Christophe and de Rijke, Maarten},

  publisher={ACM},

  booktitle={SIGIR},

  year={2018},

}

```

License

-------

pytrec\_eval is licensed under the [MIT license](LICENSE). Please note that [trec\_eval](https://github.com/usnistgov/trec_eval) is licensed separately. If you modify pytrec\_eval in any way, please link back to this repository.