Metadata-Version: 2.1
Name: ICPOptimize
Version: 2.3
Summary: Python 3 Implementation of ICP and ICPRE
Home-page: https://github.com/nicholastoddsmith/ICPOptimize
Author: Nicholas T. Smith
Author-email: nicholastsmithblog@gmail.com
License: UNKNOWN
Keywords: optimization,optimizer,linear,ICP,ICPRE
Platform: UNKNOWN
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: C
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
License-File: LICENSE

# ICPOptimize
The Iterative Constrained Pathways Optimizer

ICP is a constrained linear model optimizer built with a focus on memory efficiency, flexibility, and solution interpretability.

## Description

This repository contains implementations of the Iterative Constrained Pathways (ICP) optimization method, the ICP Rule Ensemble (ICPRE), linear classifier, regressor, and other methods. Currently, hinge and least-squares loss are supported. Support for other loss functions is planned.

Further discussion about and motivation for the methods can be found on my blog: 

[nicholastsmith.wordpress.com/2021/05/18/the-iterative-constrained-pathways-optimizer/](https://nicholastsmith.wordpress.com/2021/05/18/the-iterative-constrained-pathways-optimizer/)

## Installation

Install via PyPi:

```pip install ICPOptimize```

PyPi Project:

[https://pypi.org/project/ICPOptimize/](https://pypi.org/project/ICPOptimize/)

## Examples

```python
from ICP.Models import ICPRuleEnsemble

...

IRE = ICPRuleEnsemble().fit(A[trn], Y[trn])
YP  = IRE.predict_proba(A)
```

Further examples are available on the ICPExamples GitHub page:

[https://github.com/nicholastoddsmith/ICPExamples](https://github.com/nicholastoddsmith/ICPExamples)


