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            "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
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            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
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        "description": "Numerox is a Numerai tournament toolbox written in Python.\n\nAll you have to do is create a model. Take a look at `model`_ for examples.\n\nOnce you have a model numerox will do the rest. First download the Numerai\ndataset and then load it::\n\n    >>> import numerox as nx\n    >>> data = nx.download('numerai_dataset.zip')\n\nLet's use the logistic regression model in numerox to run 5-fold cross\nvalidation on the training data::\n\n    >>> model = nx.logistic()\n    >>> prediction = nx.backtest(model, data, tournament='bernie', verbosity=1)\n    logistic(inverse_l2=0.0001)\n           logloss     auc     acc    ystd   stats\n    mean  0.692885  0.5165  0.5116  0.0056   tourn  bernie\n    std   0.000536  0.0281  0.0215  0.0003  region   train\n    min   0.691360  0.4478  0.4540  0.0050    eras     120\n    max   0.694202  0.5944  0.5636  0.0061  consis   0.625\n\nOK, results are good enough for a demo so let's make a submission file for the\ntournament. We will fit the model on the train data and make our predictions\nfor the tournament data::\n\n    >>> prediction = nx.production(model, data, 'bernie', verbosity=1)\n    logistic(inverse_l2=0.0001)\n           logloss     auc     acc    ystd   stats\n    mean  0.692808  0.5194  0.5142  0.0063   tourn      bernie\n    std   0.000375  0.0168  0.0137  0.0001  region  validation\n    min   0.691961  0.4903  0.4925  0.0062    eras          12\n    max   0.693460  0.5553  0.5342  0.0064  consis        0.75\n\nLet's upload our predictions to enter the tournament::\n\n    >>> prediction.to_csv('logistic.csv')\n    >>> upload_id, status = nx.upload('logistic.csv', 'bernie',\n                                      public_id, secret_key)\n    metric                  value   minutes\n    concordance              True   0.0898\n    consistency              0.75   0.0898\n    originality             False   0.1783\n    validation_logloss     0.6928   0.1783\n    stakeable                True   0.1783\n\nExamples\n========\n\nHave a look at the `examples`_.\n\nInstall\n=======\n\nInstall with pip::\n\n    $ pip install numerox\n\nAfter you have installed numerox, run the unit tests (please report any\nfailures)::\n\n    >>> import numerox as nx\n    >>> nx.test()\n\nRequirements: numpy, scipy, pandas, sklearn, pytables, numerapi,\nsetuptools, requests, nose.\n\nResources\n=========\n\n- Let's `chat`_\n- See `examples`_\n- Check `what's new`_\n- Report `bugs`_\n\nSponsor\n=======\n\nThank you `Numerai`_ for funding the development of Numerox.\n\nLicense\n=======\n\nNumerox is distributed under the the GPL v3+. See LICENSE file for details.\nWhere indicated by code comments parts of NumPy are included in numerox. The\nNumPy license appears in the licenses directory.\n\n\n.. _model: https://github.com/kwgoodman/numerox/blob/master/numerox/examples/model.rst\n.. _examples: https://github.com/kwgoodman/numerox/blob/master/numerox/examples/readme.rst\n.. _chat: https://community.numer.ai/channel/numerox\n.. _bugs: https://github.com/kwgoodman/numerox/issues\n.. _what's new: https://github.com/kwgoodman/numerox/blob/master/release.rst\n.. _Numerai: https://numer.ai\n\n\n",
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        "license": "GNU General Public License v3",
        "maintainer": "Keith Goodman",
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        "name": "numerox",
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