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        "author": "HPC lab, ISTI-CNR",
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            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Information Analysis",
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        "description": "[![Build Status](https://img.shields.io/travis/hpclab/rankeval/master.svg?logo=travis)](https://travis-ci.org/hpclab/rankeval)\n[![Python version](https://img.shields.io/pypi/pyversions/rankeval.svg)](https://badge.fury.io/py/rankeval)\n[![PyPI version](https://img.shields.io/pypi/v/rankeval.svg)](https://badge.fury.io/py/rankeval)\n[![Wheel](https://img.shields.io/badge/wheels-%E2%9C%93-4c1.svg?longCache=true&logo=python&logoColor=white)](https://badge.fury.io/py/rankeval)\n[![CPython Implementation](https://img.shields.io/pypi/implementation/rankeval.svg)](https://badge.fury.io/py/rankeval)\n[![License](https://img.shields.io/badge/license-MPL%202.0-blue.svg)](https://badge.fury.io/py/rankeval)\n[![DOI](https://img.shields.io/badge/DOI-10.1145%2F3077136.3084140-blue)](https://doi.org/10.1145/3077136.3084140)\n\n<img src=\"https://github.com/hpclab/rankeval/blob/master/doc/banner.png?raw=true\">\n\n# RankEval: An Evaluation and Analysis Framework for Learning-to-Rank Solutions\n\nRankEval is an open-source tool for the analysis and evaluation of\nLearning-to-Rank models based on ensembles of regression trees. The\nsuccess of ensembles of regression trees fostered the development of\nseveral open-source libraries targeting efficiency of the learning phase\nand effectiveness of the resulting models. However, these libraries offer\nonly very limited help for the tuning and evaluation of the trained models.\n\nRankEval aims at providing a common ground for several Learning to Rank \nlibraries by providing useful and interoperable tools for a comprehensive\ncomparison and in-depth analysis of ranking models. Target audience is the \n*machine learning* (ML) and *information retrieval* (IR) communities.\n\nRankEval is available under Mozilla Public License 2.0.\n\nThe official GitHub repository is: [here](https://github.com/hpclab/rankeval).\n\nFor questions/suggestions on how to improve RankEval, send us an email: \nrankeval@isti.cnr.it\n\n## Features\n\nRankeval provides a common ground between several pre-existing tools and offers \nservices which support the interpretation of differently generated models in a \nunified environment, allowing an easy, comprehensive comparison and in-depth \nanalysis.\n\nThe main functionalities of RankEval can be summarized along five dimensions:\n- effectiveness analysis\n- feature analysis\n- structural analysis\n- topological analysis\n- interoperability among GBRT libraries\n    - support the model format of the most popular learning tools such as \n    QuickRank, RankLib, XGBoost, LightGBM, Scikit-Learn, etc\n\nThese functionalities can be applied to several models at the same time, so to \nhave a direct comparison of the analysis performed. The tool has been written \nto ensure **flexibility**, **extensibility**, and **efficiency**. \n\n## Documentation\n\nThe official API documentation is available at: [here](http://rankeval.isti.cnr.it/docs/).\nSoon on ReadTheDocs!\n\n## Installation\n\nThe library works with OpenMP so you need a compiler supporting it. \nIf your machine uses a default compiler different from GNU GCC, change it \nappropriately before proceeding with the installation:\n\n```\nexport CC=gcc-5\nexport CXX=g++-5\n```\n\nMoreover, RankEval needs the following libraries to be installed before the \ninstallation process begin:\n  - numpy >= 1.13\n  - scipy >= 0.14\n  - cython >= 0.25\n  - matplotlib >= 2.0.2\n\nRankEval can be easily installed from Python Package Index (PyPI). \nYou may download and install it by running:\n\n```pip install rankeval```\n\nAlternatively, you can build the library from source.\nBelow an example of installation.\n\n```python setup.py install```\n\nor\n\n```pip install -e .```\n\n## Development\n\nInstallation of libraries required for development (documentation generation and unittests):\n\n```pip install -e .[develop]```\n\nLocal installation of compiled libraries: \n\n```python setup.py build_ext -i```\n\nExecution of unit tests:\n\n```python setup.py test```\n\nor (if you have nose already installed):\n\n```nosetests -v```\n\n## Cite RankEval\n\nIf you use RankEval, please cite us!\n\n```\n@inproceedings{rankeval-sigir17,\n  author = {Claudio Lucchese and Cristina Ioana Muntean and Franco Maria Nardini and\n            Raffaele Perego and Salvatore Trani},\n  title = {RankEval: An Evaluation and Analysis Framework for Learning-to-Rank Solutions},\n  booktitle = {SIGIR 2017: Proceedings of the 40th International {ACM} {SIGIR}\n               Conference on Research and Development in Information Retrieval},\n  year = {2017},\n  location = {Tokyo, Japan}\n}\n```\n\n## Credits\n    - Dataset loader: https://github.com/deronnek/svmlight-loader\n    - Query id implementation: https://github.com/mblondel/svmlight-loader/pull/6\n\n",
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