Metadata-Version: 1.2
Name: numerox
Version: 4.1.3
Summary: Numerai tournament toolbox written in Python
Home-page: https://github.com/numerai/numerox
Maintainer: Keith Goodman
License: GNU General Public License v3
Description: Numerox is a Numerai tournament toolbox written in Python.
        
        All you have to do is create a model. Take a look at `model`_ for examples.
        
        Once you have a model numerox will do the rest. First download the Numerai
        dataset and then load it::
        
            >>> import numerox as nx
            >>> data = nx.download('numerai_dataset.zip')
        
        Let's use the logistic regression model in numerox to run 5-fold cross
        validation on the training data::
        
            >>> model = nx.logistic()
            >>> prediction = nx.backtest(model, data, tournament='bernie', verbosity=1)
            logistic(inverse_l2=0.0001)
                   logloss     auc     acc    ystd   stats
            mean  0.692885  0.5165  0.5116  0.0056   tourn  bernie
            std   0.000536  0.0281  0.0215  0.0003  region   train
            min   0.691360  0.4478  0.4540  0.0050    eras     120
            max   0.694202  0.5944  0.5636  0.0061  consis   0.625
        
        OK, results are good enough for a demo so let's make a submission file for the
        tournament. We will fit the model on the train data and make our predictions
        for the tournament data::
        
            >>> prediction = nx.production(model, data, 'bernie', verbosity=1)
            logistic(inverse_l2=0.0001)
                   logloss     auc     acc    ystd   stats
            mean  0.692808  0.5194  0.5142  0.0063   tourn      bernie
            std   0.000375  0.0168  0.0137  0.0001  region  validation
            min   0.691961  0.4903  0.4925  0.0062    eras          12
            max   0.693460  0.5553  0.5342  0.0064  consis        0.75
        
        Let's upload our predictions to enter the tournament::
        
            >>> prediction.to_csv('logistic.csv')
            >>> upload_id, status = nx.upload('logistic.csv', 'bernie',
                                              public_id, secret_key, model_id)
            metric                  value   minutes
            concordance              True   0.0898
            consistency              0.75   0.0898
            originality             False   0.1783
            validation_logloss     0.6928   0.1783
            stakeable                True   0.1783
        
        Examples
        ========
        
        Have a look at the `examples`_.
        
        Install
        =======
        
        Install with pip::
        
            $ pip install numerox
        
        After you have installed numerox, run the unit tests (please report any
        failures)::
        
            >>> import numerox as nx
            >>> nx.test()
        
        Requirements: numpy, scipy, pandas, sklearn, pytables, numerapi,
        setuptools, requests, nose.
        
        Resources
        =========
        
        - Let's `chat`_
        - See `examples`_
        - Check `what's new`_
        - Report `bugs`_
        
        Sponsor
        =======
        
        Thank you `Numerai`_ for funding the development of Numerox.
        
        License
        =======
        
        Numerox is distributed under the the GPL v3+. See LICENSE file for details.
        Where indicated by code comments parts of NumPy are included in numerox. The
        NumPy license appears in the licenses directory.
        
        
        .. _model: https://github.com/kwgoodman/numerox/blob/master/numerox/examples/model.rst
        .. _examples: https://github.com/kwgoodman/numerox/blob/master/numerox/examples/readme.rst
        .. _chat: https://community.numer.ai/channel/numerox
        .. _bugs: https://github.com/kwgoodman/numerox/issues
        .. _what's new: https://github.com/kwgoodman/numerox/blob/master/release.rst
        .. _Numerai: https://numer.ai
        
Platform: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
