Overview of mgcpy¶
mgcpy is a Python package containing tools for multiscale graph correlation and other statistical tests, that is capable of dealing with high dimensional and multivariate data.
Motivation¶
Examining and identifying relationships between variables is critical for many scientists to definitely establishing causality and deciphering these relationships in further studies. To approach this problem, the most commonly used statistic utilized is Pearson’s Product-Moment Correlation (Pearson, 1895) but the test fails to address some of the issues that data scientists face today (Vogelstein et al., 2016). Other tests conventionally used include “energy statistics” such as Dcorr; kernel-based approaches such as Hilbert Schmidt Independence Criterion (HSIC) (Gretton and GyĂśrfi, 2010) which has recently been shown to be equivalent to “energy statistics” (Sejdinovic et al., 2013)(Shen and Vogelstein, 2018); Heller, Heller, and Gorfine’s test (HHG) (Heller et al., 2012), and many others. These tests perform empirically well on either high dimensional linear data or low dimensional nonlinear data.No approach works well on high dimensional nonlinear data, and no approach addresses issues on how to interpret the data.
Multiscale graph correlation (MGC) attempts to alleviate these issues. The test utilizes features of other techniques such ask-nearest neighbors, kernel methods, and multiscale analysis to detect relationships (Vogelstein et al., 2016) in all types of data, including high dimensional nonlinear data. The test is also computationally efficient, requiring about half or one third of the number of samples to achieve the same statistical power (Vogelstein et al.,2016). In addition, the test provides information about the data’s geometry (Vogelsteinet al., 2016), allowing for more informed decision making of the underlying relationships in the data
About¶
mgcpy
aims to be a comprehensive independence testing package including all of the
commonly used independence tests as mentioned above 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.
Free software¶
mgcpy
is free software; you can redistribute it and/or modify it under the
terms of the Apache-2.0. We welcome contributions.
Join us on GitHub.
Documentation¶
mgcpy
is a hypothesis testing package in python.
Install¶
Below we assume you have the default Python environment already configured on
your computer and you intend to install mgcpy
inside of it. If you want
to create and work with Python virtual environments, please follow instructions
on venv and virtual
environments.
First, make sure you have the latest version of pip
(the Python package manager)
installed. If you do not, refer to the Pip documentation and install pip
first.
Install the released version¶
Install the current release of mgcpy
with pip
:
$ pip install mgcpy
To upgrade to a newer release use the --upgrade
flag:
$ pip install --upgrade mgcpy
If you do not have permission to install software systemwide, you can
install into your user directory using the --user
flag:
$ pip install --user mgcpy
Alternatively, you can manually download mgcpy
from
GitHub or
PyPI.
To install one of these versions, unpack it and run the following from the
top-level source directory using the Terminal:
$ pip install .
Install from Github¶
To install from Github, run the following from the top-level source directory using the Terminal:
$ git clone https://github.com/neurodata/mgcpy
$ cd mgcpy
$ python3 setup.py install
sudo
, if requiredpython3 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](https://docs.docker.com/install/)
- 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
ordocker pull tpsatish95/mgcpy:development
docker run -it --rm -p 8888:8888 --name mgcpy-env tpsatish95/mgcpy:latest
- 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.
- Do
Python package dependencies¶
mgcpy requires the following packages:
- numpy
- scikit-learn
- scipy
- Cython
- pandas
- h5py
- seaborn
Hardware requirements¶
mgcpy package requires only a standard computer with enough RAM to support the in-memory operations.
OS Requirements¶
This package is supported for macOS and partly on Linux.
Testing¶
mgcpy uses the Python pytest
testing package. If you don’t already have
that package installed, follow the directions on the pytest homepage.
License¶
mgcpy is distributed with Apache 2.0 license.
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