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 | ✔️ | ✔️ | ✔️ |
| Parallel programming | ✔️ | ✔️ | ✔️ |
| p-value | ✔️ | ✔️ | ❌ |
| Limit distribution | Two-sample test only | Independence test only | ❌ |
| Censored data | ❌ | ❌ | Comming soon |
| Interaction screening | ❌ | ❌ | ✔️ |
| GWAS optimization | ❌ | ❌ | 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 zhuj1jqx@gmail.com
Release files for Ball 0.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ball-0.3.0.tar.gz | 77.0 kB | Details |
Built distributions (wheels)
Total release size: 16.3 MB
Release files / ball-0.3.0.tar.gz
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| Size | 77.0 kB |
| Tags | Source |
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Transparency logRelease files / ball-0.3.0-cp310-cp310-macosx_10_9_x86_64.whl
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Transparency logRelease files / ball-0.3.0-cp39-cp39-win_amd64.whl
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Transparency logRelease files / ball-0.3.0-cp39-cp39-macosx_11_0_arm64.whl
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| Size | 140.8 kB |
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| Tags | CPython 3.9 macOS 10.9+ x86-64 |
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