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Python 3 Implementation of ICP and ICPRE

Project description

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/

Installation

Install via PyPi:

pip install ICPOptimize

PyPi Project:

https://pypi.org/project/ICPOptimize/

Examples

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

Project details


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Source Distribution

ICPOptimize-2.3.tar.gz (194.3 kB view hashes)

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