.. image:: https://travis-ci.org/numerai/numerox.svg?branch=master :target: https://travis-ci.org/numerai/numerox .. image:: https://ci.appveyor.com/api/projects/status/github/kwgoodman/numerox?svg=true&passingText=passing&failingText=failing&pendingText=pending :target: https://ci.appveyor.com/project/kwgoodman/numerox .. image:: https://img.shields.io/pypi/v/numerox.svg :target: https://pypi.python.org/pypi/numerox/ numerox

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