Metadata-Version: 1.1
Name: numerox
Version: 0.6.0
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('numerai_dataset.zip')
            >>> data = nx.load_zip('numerai_dataset.zip')
            >>> data
            rows      637184
            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=0.0001)
                  logloss   auc     acc     ystd   stats
            mean  0.692885  0.5165  0.5116  0.0056  region     train
            std   0.000536  0.0281  0.0215  0.0003    eras       120
            min   0.691360  0.4478  0.4540  0.0050  sharpe  0.488866
            max   0.694202  0.5944  0.5636  0.0061  consis  0.691667
        
        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, verbosity=1)
            logistic(inverse_l2=0.0001)
                  logloss   auc     acc     ystd   stats
            mean  0.692808  0.5194  0.5142  0.0063  region  validation
            std   0.000375  0.0168  0.0137  0.0001    eras          12
            min   0.691961  0.4903  0.4925  0.0062  sharpe    0.903277
            max   0.693460  0.5553  0.5342  0.0064  consis    0.916667
        
        Let's upload our predictions to enter the tournament::
        
            >>> prediction.to_csv('logistic.csv')  # 6 decimal places by default
            >>> upload_id, status = nx.upload('logistic.csv', public_id, secret_key)
            metric                  value   minutes
            concordance              True   0.0898
            consistency           91.6667   0.0898
            originality             False   0.1783
            validation_logloss     0.6928   0.1783
            controlling capital     False   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: python, setuptools, numpy, pandas, pytables, sklearn, numerapi,
        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 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/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
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
