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 live, test, train, validation rows 884544 era 98, [era1, eraX] x 50, min 0.0000, mean 0.4993, max 1.0000 y mean 0.499961, fraction missing 0.3109
Let’s use the logistic regression model in numerox to run 5-fold cross validation on the training data:
>>> model = nx.model.logistic() >>> prediction = nx.backtest(model, data, verbosity=1) logistic(inverse_l2=1e-05) logloss auc acc ystd mean 0.692974 0.5226 0.5159 0.0023 | region train std 0.000224 0.0272 0.0205 0.0002 | eras 85 min 0.692360 0.4550 0.4660 0.0020 | consis 0.7647 max 0.693589 0.5875 0.5606 0.0027 | 75th 0.6931
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 0.692993 0.5157 0.5115 0.0028 | region validation std 0.000225 0.0224 0.0172 0.0000 | eras 12 min 0.692440 0.4853 0.4886 0.0028 | consis 0.7500 max 0.693330 0.5734 0.5555 0.0028 | 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.
Resources
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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.
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