This provides several algorithms for finding approximate anomalous regions.
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README.md

SampleScan

This is a python wrapper around several anomaly detection algorithms written in c++. These algorithms are from the paper Scalable Spatial Scan Statistics through Sampling. To compile this you will need:

  • boost.python
  • python 2.7
  • scons

#Instructions for OSX Assuming you have brew installed:

> brew install python
> brew install boost --with-python
> brew install scons

Then you can cd to the directory and run:

> scons

This will create a build directory containing build/release and build/debug builds. You can use this library by adding the build/release/pywrapper directory to your python path. So:

> export PYTHONPATH=build/release/pywrapper:${PYTHONPATH}

You should then be able to use this library as a standard python module by doing: import eps_scan Linux instructions should be basically identical. (I have run this on arch linux and ubuntu)

Calling eps scan

Example code:

import eps_scan
import random
#Generate a list of random points with random anomalies
pts = [eps_scan.Point(random.random(), random.random(), bool(random.randint(0, 1))) for i in xrange(1000)]
#Run the algorith with a net size of 100 and a sample size of 1000 and print the found region.
#The last parameter prevents us from considering regions that are too large or small since the approximation breaks down there.
print eps_scan.netDisks(pts, 100, 1000, .01) # runs on disks
print eps_scan.netRectsG(pts, 100, 1000, .01) # runs on axis aligned rectangles. 
# For the rectangle algorithm run:
#export OMP_NUM_THREADS=n
# where n is the number of threads you want to use with the algorithm.

Try checking the example directory or the testing directory for more examples. The api is defined in the pywrapper directory so you can also check that out.