Numerox is a Numerai tournament toolbox written in Python
Project 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.
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