Ball 0.3.0

Ball: A Python Package for Detecting Distribution Difference and Association in Metric Spaces

Ball Statistics in Python

Introdution

The fundamental problems for data mining, statistical analysis, and machine learning are:

• whether several distributions are different?
• whether random variables are dependent?
• how to pick out useful variables/features from a high-dimensional data?

These issues can be tackled by Ball statistics, which enjoy following admirable advantages:

• available for most of datasets (e.g., traditional tabular data, brain shape, functional connectome, wind direction and so on)
• insensitive to outliers, distribution-free and model-free;
• theoretically guaranteed and computationally efficient.

Installation

Pypi version

To install the Ball Python package from Pypi, just run:

pip install Ball

• Git version (development)

Windows

Ball support compilation with MinGW. You need to install MinGW (https://sourceforge.net/projects/mingw/). Next, you should clone or download the Ball repo. Then run the following from the root of the Ball directory:

sh configure.sh your_python_path your_python_version_number # e.g.  sh configure.sh C:/anaconda3 35

Overview: Ball package

Three most importance functions in Ball:

	bd.test	bcov.test	bcorsis
Feature	Hypothesis test	Hypothesis test	Feature screening
Type	Test of equal distributions	Test of independence	SIS and ISIS
Optional weight	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
Parallel programming	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
p-value	:heavy_check_mark:	:heavy_check_mark:	:x:
Limit distribution	Two-sample test only	Independence test only	:x:
Censored data	:x:	:x:	Comming soon
Interaction screening	:x:	:x:	:heavy_check_mark:
GWAS optimization	:x:	:x:	Comming soon

• SIS: Sure Independence Screening
• ISIS: Iterative Sure Independence Screening (SIS)
• GWAS: Genome-Wide Association Study

Reference

• Pan, Wenliang; Tian, Yuan; Wang, Xueqin; Zhang, Heping. Ball Divergence: Nonparametric two sample test. Ann. Statist. 46 (2018), no. 3, 1109--1137. doi:10.1214/17-AOS1579. https://projecteuclid.org/euclid.aos/1525313077
• Wenliang Pan, Xueqin Wang, Weinan Xiao & Hongtu Zhu (2018) A Generic Sure Independence Screening Procedure, Journal of the American Statistical Association, DOI: 10.1080/01621459.2018.1462709
• Wenliang Pan, Xueqin Wang, Heping Zhang, Hongtu Zhu & Jin Zhu (2019) Ball Covariance: A Generic Measure of Dependence in Banach Space, Journal of the American Statistical Association, DOI: 10.1080/01621459.2018.1543600
• Jin, Z., Wenliang P., Wei Z., and Xueqin W. (2018). Ball: An R package for detecting distribution difference and association in metric spaces. arXiv preprint arXiv:1811.03750. URL http://arxiv.org/abs/1811.03750.

Bug report

If you find any bugs, or if you experience any crashes, please report to us. If you have any questions just ask, we won't bite. Open an issue or send email to Jin Zhu at [e-mail]