This code was written by Paul Duan (email@paulduan.com) and Benjamin Solecki (bensolucky@gmail.com). It provides our winning solution to the Amazon Employee Access Challenge. Our code is currently not merged. You'll find Benjamin's code in the BSMan/ folder, which needs to be run separately.

[python] classifier.py [-h] [-d] [-i ITER] [-f OUTPUTFILE] [-g] [-m] [-n] [-s] [-v] [-w]
Parameters for the script.
optional arguments:
 -h, --help show this help message and exit
 -d, --diagnostics Compute diagnostics.
 -i ITER, --iter ITER Number of iterations for averaging.
 -f OUTPUTFILE, --outputfile OUTPUTFILE
 Name of the file where predictions are saved.
 -g, --grid-search Use grid search to find best parameters.
 -m, --model-selection
 Use model selection.
 -n, --no-cache Use cache.
 -s, --stack Use stacking.
 -v, --verbose Show computation steps.
 -w, --fwls Use metafeatures.

To directly generate predictions on the test set without computing CV metrics, simply run:

python classifier.py -i0 -f[output_filename]

This script will launch Paul's model, which incorporates some of Benjamin's features. Benjamin's model is in the BSMan folder and can be run this way:

(in BSMan/)
[python] logistic.py log 75
[python] ensemble.py

The output of our models is then combined by simple standardization then weighted averaging, using 2/3 Paul's model and 1/3 Benjamin's.

This code requires Python, numpy/scipy, scikit-learn, and pandas for some of the external code (this dependency will be removed in the future).
It has been tested under Mac OS X with Python v.7.x, scikit-learn 0.13, numpy 0.17, and pandas 0.11.

This content is released under the MIT Licence.