abess Python Package
Project description
abess: R & Python Softwares for Best-Subset Selection in Polynomial Time
Best-subset selection aims to find a small subset of predictors such that the resulting linear model is expected to have the most desirable prediction accuracy. This project implements a polynomial algorithm proposed by Zhu et al (2020) to solve the problem. More over, the softwares includes helpful features for high-dimensional data analysis:
- Linear regression, classification, counting-response modeling, censored-response modeling, multi-response modeling (multi-tasks learning)
- sure independence screening
- nuisance penalized regression
Installation
Python-package
Install the stable version of Python-package from Pypi with:
pip install abess
Reference
A polynomial algorithm for best-subset selection problem. Junxian Zhu, Canhong Wen, Jin Zhu, Heping Zhang, Xueqin Wang. Proceedings of the National Academy of Sciences Dec 2020, 117 (52) 33117-33123; DOI: 10.1073/pnas.2014241117
Fan, J. and Lv, J. (2008), Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70: 849-911. https://doi.org/10.1111/j.1467-9868.2008.00674.x
Qiang Sun & Heping Zhang (2020) Targeted Inference Involving High-Dimensional Data Using Nuisance Penalized Regression, Journal of the American Statistical Association, DOI: 10.1080/01621459.2020.1737079
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for abess-0.0.2-cp39-cp39-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3035c4c24cd3d77b8c59db0023d40241f0f6c3bebabc4208341c2f17365fc2e5 |
|
MD5 | cd0a9a00236a58e91d86ce1fda12140f |
|
BLAKE2b-256 | 4135decf1c4f3af0c3a16cf18fcbb0219468fa502b61f59c5ae45fde8a9130f9 |
Hashes for abess-0.0.2-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1efccfd50f277b2675e56c37ee9271f900e168eab76cee135b9750a681e10cf7 |
|
MD5 | 86bc25a9fef15a2cb9bd84cb5c51e1b5 |
|
BLAKE2b-256 | ac7d6085f7bfb5ec60c286fc3ddb1cb7222df707dc1d8e6d58b2ce599e3e4203 |
Hashes for abess-0.0.2-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 05f5ce97e408a9ac8e01fbaedeee84b1f63850801c822b31c0a6d4adf6e6a5e6 |
|
MD5 | d7b24c82bfa0cf3d5d21fd45b8f4dd08 |
|
BLAKE2b-256 | 790d133a2c40c6e05d4a5b71267594a4bcdd203cb22d352936922dcffb443514 |
Hashes for abess-0.0.2-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5af29769329b953e2b86728b82e4355f1dc2ecf280e5c919171b8042a77859a0 |
|
MD5 | 666c1c12e49ca4d839fc159c266526f6 |
|
BLAKE2b-256 | 57dcc7a8e9899a25af43465d484646a4533b2cc531b8a59d8451943239183fbd |
Hashes for abess-0.0.2-cp35-cp35m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5dbcc719bd4645c31409b05b674dd45b9eb8fb1d3284cb49c9cd59ade494a299 |
|
MD5 | bd0f69dc64a199615c9e15d8ef64a0c9 |
|
BLAKE2b-256 | e177b8468b566590638a1be7d96a91ec42102c91cc9a4a144f4e5dfe58f9c52f |