Numerai tournament toolbox written in Python
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) 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
Have a look at the examples.
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.
Thank you Numerai for funding the development of Numerox.
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.