A digit doctoring detection package
Project description
DrDigit is a digit doctoring detection package at an early stage. Interested in contributing? Please feel free to contact me, e.g. by commenting on the issue “Contributors welcome!” at https://github.com/brezniczky/drdigit/issues/1.
Requirements
DrDigit requires Python 3.5 or later.
Concept
The tests are based on the statistics of digits which are assumed to have a uniform distribution. Near-uniform distributions can be obtained by looking at the last digits of sufficiently large values - such as vote counts (possibly above 100).
On a smaller scale, you can query for the probablity of a digit sequence using probability mass functions represented by Python functions.
There are larger scale tests for a sequence of digit groups. This is so to support situations where different groups are expected to be doctored by different people - testing for an overarching, consistent anomaly could be too strict in such cases.
Based on the current features (entropy, digit repetition, coincident digits in parallel sequences), it is possible to sort a data frame containing digit groups by probability, so then it is possible to inspect if there is any apparent sanity behind the doctoring.
A couple of advice
Handle results with care, there is always some uncertainity
Try to focus on interesting groups, this should yield much sharper results
Quick start
DrDigit can be installed using pip:
$ pip install drdigit-brezniczky $ ipython
Digit entropy behaves a little weirdly when different digit sequence lengths are considered - isn’t the sequence 1, 2 as diverse as possible?
Python 3.5.2 (default, Nov 12 2018, 13:43:14) Type ‘copyright’, ‘credits’ or ‘license’ for more information IPython 7.7.0 – An enhanced Interactive Python. Type ‘?’ for help.
In [1]: import drdigit as drd
In [2]: help(drd)
In [3]: print(drd.get_entropy([1, 2])) 0.6931471805599453
In [4]: print(drd.get_entropy([1, 1, 2, 2])) 0.6931471805599453
Probabilities are often more suited for a comparison:
In [6]: drd.prob_of_entr(2, drd.get_entropy([1, 2])) cdf for 2 was generated Out[6]: 1.0
In [7]: drd.prob_of_entr(4, drd.get_entropy([1, 1, 2, 2])) cdf for 4 was generated Out[7]: 0.0624
Indeed, the latter sequence is unusually repetitive.
More examples to follow, for now you can have a look at the Kaggle notebook at https://www.kaggle.com/brezniczky/poland-2019-ep-elections-doctoring-quick-check or around https://github.com/brezniczky/ep_elections_2019_hun/blob/master/PL/ for instance in the process_data.py file.
Tests
Coming soon …
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