Metadata-Version: 1.1
Name: numerox
Version: 0.1.0
Summary: Numerox is a Numerai tournament toolbox written in Python
Home-page: https://github.com/kwgoodman/numerox
Author: Keith Goodman
Author-email: UNKNOWN
License: GNU General Public License v3
Description-Content-Type: UNKNOWN
Description: Numerox is a Numerai tournament toolbox written in Python.
        
        All you have to do is create a model. Take a look at `model.py`_ 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
            >>> nx.download_dataset('numerai_dataset.zip')
            >>> data = nx.load_zip('numerai_dataset.zip')
            >>> data
            region    train, validation, test, live
            rows      637205
            era       133, [era1, eraX]
            x         50, min 0.0000, mean 0.5025, max 1.0000
            y         mean 0.499924, fraction missing 0.3095
        
        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, verbosity=1)
            logistic(inverse_l2=1e-05)
                  logloss   auc     acc     ystd
            mean  0.693103  0.5159  0.5114  0.0008  |  region   train
            std   0.000080  0.0289  0.0219  0.0000  |  eras     120
            min   0.692874  0.4384  0.4446  0.0007  |  consis   0.7000
            max   0.693323  0.5962  0.5626  0.0009  |  75th     0.6932
        
        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)
            logistic(inverse_l2=1e-05)
                  logloss   auc     acc     ystd
            mean  0.693090  0.5178  0.5137  0.0010  |  region   validation
            std   0.000060  0.0171  0.0140  0.0000  |  eras     12
            min   0.692950  0.4891  0.4927  0.0010  |  consis   0.9167
            max   0.693192  0.5556  0.5350  0.0010  |  75th     0.6931
            >>> prediction.to_csv('logistic.csv')  # 6 decimal places by default
        
        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: python, setuptools, numpy, pandas, pytables, sklearn, requests,
        nose.
        
        Optional: xgboost
        
        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 and SciPy are included in
        numerox. Their licenses appear in the licenses directory.
        
        
        .. _model.py: https://github.com/kwgoodman/numerox/blob/master/numerox/model.py
        .. _examples: https://github.com/kwgoodman/numerox/blob/master/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
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
