https://github.com/ustunb/slim-python
simple customizable scoring systems in python
https://github.com/ustunb/slim-python
Last synced: about 1 month ago
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simple customizable scoring systems in python
- Host: GitHub
- URL: https://github.com/ustunb/slim-python
- Owner: ustunb
- License: gpl-3.0
- Created: 2016-07-11T20:37:18.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2023-10-18T11:36:05.000Z (over 2 years ago)
- Last Synced: 2024-04-18T18:01:23.316Z (almost 2 years ago)
- Language: Python
- Homepage:
- Size: 2.66 MB
- Stars: 41
- Watchers: 10
- Forks: 12
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-machine-learning-interpretability - Super-sparse Linear Integer models - SLIMs -  | "a package to learn customized scoring systems for decision-making problems.” | (Technical Resources / Open Source/Access Responsible AI Software Packages)
README
``slim-python`` is a package to learn customized *scoring systems* for decision-making problems.
These are simple decision aids that let users make yes-no predictions by adding and subtracting a few small numbers. 
SLIM is designed to learn the most accurate scoring system for a given dataset and set of constraints. These models are produced by solving a hard optimization problem that directly optimizes for accuracy, sparsity, and customized constraints (e.g., hard limits on model size, TPR, FPR).
## Requirements
``slim-python`` was developed using Python 2.7.11 and CPLEX 12.6.2.
### CPLEX
*CPLEX* is cross-platform commercial optimization tool with a Pytho API. It is freely available to students and faculty members at accredited institutions as part of the IBM Academic Initiative. To get CPLEX:
1. Join the [IBM Academic Initiative](http://www-304.ibm.com/ibm/university/academic/pub/page/mem_join). Note that it may take up to a week to obtain approval.
2. Download *IBM ILOG CPLEX Optimization Studio V12.6.1* (or higher) from the [software catalog](https://www-304.ibm.com/ibm/university/academic/member/softwaredownload)
3. Install the file on your computer. Note mac/unix users will [need to install a .bin file](http://www-01.ibm.com/support/docview.wss?uid=swg21444285).
4. Setup the CPLEX Python modules [as described here here](http://www.ibm.com/support/knowledgecenter/SSSA5P_12.6.3/ilog.odms.cplex.help/CPLEX/GettingStarted/topics/set_up/Python_setup.html).
Please check the [CPLEX user manual](http://www-01.ibm.com/support/knowledgecenter/SSSA5P/welcome) or the [CPLEX forums](https://www.ibm.com/developerworks/community/forums/html/forum?id=11111111-0000-0000-0000-000000002059) if you have problems installing CPLEX.
## Citation
If you use SLIM for academic research, please cite [our paper](http://http//arxiv.org/abs/1502.04269/)!
```
@article{
ustun2015slim,
year = {2015},
issn = {0885-6125},
journal = {Machine Learning},
doi = {10.1007/s10994-015-5528-6},
title = {Supersparse linear integer models for optimized medical scoring systems},
url = {http://dx.doi.org/10.1007/s10994-015-5528-6},
publisher = { Springer US},
author = {Ustun, Berk and Rudin, Cynthia},
pages = {1-43},
language = {English}
}
```