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The graph identification algorithm from my thesis

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# Graph Identification

A graph algorithm to manage the identification of individuals in a population using automatic pairwise decision algorithms with a humans in the loop. It is agnostic to the specific ranking and verification algorithms. In fact, it can work without a ranking or verification algorithm, but in that case all reviews will have to be manual, and it will be difficult to prioritize which pairs of annotations (typically images) to look at first.

This is the graph identification described in Chapter 5 of [my thesis](https://github.com/Erotemic/crall-thesis-2017/blob/master/crall-thesis_2017-08-10_compressed.pdf). Viewing this PDF online can be slow, so I’ve linked there raw text [here](https://github.com/Erotemic/crall-thesis-2017/blob/master/chapter5-graphid.tex).

# General Information

This repo is currently a work in progress.

Helpful commands I’m currently using in development and debugging. Perhaps they will be someone illustrative of what this package is trying to do.

` python -m graphid.demo.dummy_infr demodata_infr --show python -m graphid.demo.dummy_infr demodata_infr --num_pccs=25 --show python -m graphid.demo.dummy_infr demodata_infr --num_pccs=100 --show `

This README is a mess. Why not look at [this Jupyter notebook](notebooks/core_example.ipynb) in the meantime.

# Installation

Once this package becomes stable you can install via pip install graphid. However, this will currently give you an older version of the project I uploaded to reserve the name.

# Dependencies

`bash sudo apt-get install -y graphviz libgraphviz-dev pip install graphviz pip install -e . `

This project is Python 3.6+, Python 2 is not supported.

If you want to be able to draw the graphs, you must install graphviz, which is needed by pygraphviz.

I’m currently having trouble getting this to work on windows due to pygraphviz.

Conda can be used to install pygraphviz on windows? conda install -c marufr pygraphviz

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