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        "author": "Nicolas Hug",
        "author_email": "nh.nicolas.hug@gmail.com",
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            "Intended Audience :: Developers",
            "Intended Audience :: Education",
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            "License :: OSI Approved",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering"
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        "description": "[![GitHub version](https://badge.fury.io/gh/Niourf%2Frecsys.svg)](https://badge.fury.io/gh/Niourf%2Frecsys)\n[![Documentation Status](https://readthedocs.org/projects/recsys/badge/?version=latest)](http://recsys.readthedocs.io/en/latest/?badge=latest)\n[![Build Status](https://travis-ci.org/Niourf/RecSys.svg?branch=master)](https://travis-ci.org/Niourf/RecSys)\n[![python_versions](https://img.shields.io/badge/python-2.7%2C%203.4%2C%203.5-blue.svg)]\n(https://pypi.python.org/pypi/recsys/)\n[![license](https://img.shields.io/badge/license-GPLv3-blue.svg)](https://github.com/Niourf/RecSys/blob/master/LICENSE.md)\n\n\nRecSys\n======\n\nauthor: Nicolas Hug\n\nOverview\n--------\n\nRecSys is an open source Python package that provides with tools to build and\nevaluate the performance of many recommender system prediction algorithms. Its\ngoal is to make life easy(-ier) for reseachers and students who want to play\naround with new recommender algorithm ideas.\n\nA strong emphasis is laid on\n[documentation](http://recsys.readthedocs.io/en/latest/index.html), which we\nhave tried to make as clear and precise as possible by pointing out every\ndetail of the algorithms, in order for the practitioner to have perfect\ncontrol over his experiments.\n\nFeatures\n--------\n\n- [Dataset\n  handling](http://recsys.readthedocs.io/en/latest/getting_started.html) is made easy.\n- Various ready-to-use [prediction\n  algorithms](http://recsys.readthedocs.io/en/latest/prediction_algorithms.html).\n- Easy to implement [new algorithm\n  ideas](http://recsys.readthedocs.io/en/latest/building_custom_algo.html).\n- Evaluate,\n  [analyse](http://nbviewer.jupyter.org/github/Niourf/RecSys/tree/master/examples/notebooks/KNNBasic_analysis.ipynb/)\n  and\n  [compare](http://nbviewer.jupyter.org/github/Niourf/RecSys/tree/master/examples/notebooks/Compare.ipynb/) the algorithms performance.\n\nInstallation / Usage\n--------------------\n\nPlease, use a [virtual env](\nhttp://docs.python-guide.org/en/latest/dev/virtualenvs/)\n\nTo install from [PyPI](https://pypi.python.org/pypi/recsys/), use pip\n(recommended):\n\n    $ pip install recsys\n\nOr clone the repo and build from the sources (you'll need Cython and numpy\nfirst):\n\n    $ git clone https://github.com/Niourf/recsys.git\n    $ python setup.py install\n\nExample\n-------\n\n```python\nfrom recsys import SVD\nfrom recsys import Dataset\nfrom recsys import evaluate\n\n\n# Load the movielens-100k dataset and split it into 3 folds for\n# cross-validation.\ndata = Dataset.load_builtin('ml-100k')\ndata.split(n_folds=3)\n\n# We'll use the famous SVD algorithm.\nalgo = SVD()\n\n# Evaluate performances of our algorithm on the dataset.\nperf = evaluate(algo, data, measures=['RMSE', 'MAE'])\n\nprint(perf['RMSE'])\nprint(perf['MAE'])\n```\n\nOutput:\n\n```\n--------------------\nfold 0\nRMSE: 0.9461\nMAE: 0.7471\n--------------------\nfold 1\nRMSE: 0.9485\nMAE: 0.7481\n--------------------\nfold 2\nRMSE: 0.9373\nMAE: 0.7389\n--------------------\nmean RMSE : 0.9440\nmean MAE : 0.7447\n[0.94610849207651793, 0.94851906980098399, 0.93725513525972337]\n[0.74705780800352328, 0.74810449832136583, 0.73891237929484566]\n```\n\nDocumentation, Getting Started\n------------------------------\n\nThe documentation with many usage examples is available\n[online](http://recsys.readthedocs.io/en/latest/index.html) on ReadTheDocs.\n\nLicense\n-------\n\nThis project is licensed under the GPLv3 license - see the LICENSE.md file for\ndetails.\n\nAcknowledgements:\n----------------\n\n- [Pierre-Fran\u00e7ois Gimenez](https://github.com/PFgimenez), for his valuable\n  insights on software design.\n\nContributing\n------------\n\nAny kind of feedback would be greatly appreciated (software design,\ndocumentation, improvement ideas, spelling, etc...). Please feel free to\ncontribute!",
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