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A set of tools in Python for multiscale graph correlation and other statistical tests

Project description

The R version is available on CRAN and https://github.com/neurodata/r-mgc. The MATLAB version is available at https://github.com/neurodata/mgc-matlab.

mgcpy

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mgcpy is a Python package containing tools for independence testing using multiscale graph correlation and other statistical tests, that is capable of dealing with high dimensional and multivariate data.

Overview

mgcpy aims to be a comprehensive independence testing package including commonly used independence tests and additional functionality such as two sample independence testing and a novel random forest-based independence test. These tests are not only included to benchmark MGC but to have a convenient location for users if they would prefer to utilize those tests instead. The package utilizes a simple class structure to enhance usability while also allowing easy extension of the package for developers. The package can be installed on all major platforms (e.g. BSD, GNU/Linux, OS X, Windows)from Python Package Index (PyPI) and GitHub.

Documenation

The official documentation with usage is at: https://mgc.neurodata.io/ ReadTheDocs: https://mgcpy.readthedocs.io/en/latest/

System Requirements

Hardware requirements

mgcpy package requires only a standard computer with enough RAM to support the in-memory operations.

Software requirements

OS Requirements

This package is supported for macOS and Linux. The package has been tested on the following systems:

  • macOS: Mojave (10.14.1)
  • Linux: Ubuntu 16.04

Python Dependencies

mgcpy mainly depends on the Python scientific stack.

numpy
scipy
Cython
scikit-learn
pandas
seaborn

Installation Guide:

Install from PyPi

pip3 install mgcpy

Install from Github

git clone https://github.com/neurodata/mgcpy
cd mgcpy
python3 setup.py install
  • sudo, if required
  • python3 setup.py build_ext --inplace # for cython, if you want to test in-place, first execute this

Setting up the development environment:

  • To build image and run from scratch:

    • Install docker
    • Build the docker image, docker build -t mgcpy:latest .
      • This takes 10-15 mins to build
    • Launch the container to go into mgcpy's dev env, docker run -it --rm --name mgcpy-env mgcpy:latest
  • Pull image from Dockerhub and run:

    • docker pull tpsatish95/mgcpy:latest or docker pull tpsatish95/mgcpy:development
    • docker run -it --rm -p 8888:8888 --name mgcpy-env tpsatish95/mgcpy:latest or docker run -it --rm -p 8888:8888 --name mgcpy-env tpsatish95/mgcpy:development
  • To run demo notebooks (from within Docker):

    • cd demos
    • jupyter notebook --ip 0.0.0.0 --no-browser --allow-root
    • Then copy the url it generates, it looks something like this: http://(0de284ecf0cd or 127.0.0.1):8888/?token=e5a2541812d85e20026b1d04983dc8380055f2d16c28a6ad
    • Edit this: (0de284ecf0cd or 127.0.0.1) to: 127.0.0.1, in the above link and open it in your browser
    • Then open mgc.ipynb
  • To mount/load local files into docker container:

    • Do docker run -it --rm -v <local_dir_path>:/root/workspace/ -p 8888:8888 --name mgcpy-env tpsatish95/mgcpy:latest, replace <local_dir_path> with your local dir path.
    • Do cd ../workspace when you are inside the container to view the mounted files. The mgcpy package code will be in /root/code directory.

MGC Algorithm's Flow

MGCPY Flow

Power Curves

License

This project is covered under the Apache 2.0 License.

Project details


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