https://github.com/mert-byrktr/pso-hyperparameter-selection
Hyperparameter selection on machine learning models using Particle Swarm Optimization
https://github.com/mert-byrktr/pso-hyperparameter-selection
hyperparameter-optimization hyperparameter-search hyperparameter-tuning machine-learning optimization optimization-algorithms particle-swarm-optimization pso pso-algorithm python swarm-intelligence swarm-intelligence-algorithms
Last synced: 15 days ago
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Hyperparameter selection on machine learning models using Particle Swarm Optimization
- Host: GitHub
- URL: https://github.com/mert-byrktr/pso-hyperparameter-selection
- Owner: mert-byrktr
- Created: 2024-02-23T14:07:41.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-26T22:43:29.000Z (12 months ago)
- Last Synced: 2025-05-07T03:01:51.459Z (15 days ago)
- Topics: hyperparameter-optimization, hyperparameter-search, hyperparameter-tuning, machine-learning, optimization, optimization-algorithms, particle-swarm-optimization, pso, pso-algorithm, python, swarm-intelligence, swarm-intelligence-algorithms
- Language: Python
- Homepage:
- Size: 28.3 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Particle Swarm Optimization (PSO) Hyperparameter Optimization
This Python module implements hyperparameter optimization using Particle Swarm Optimization (PSO) for various machine learning algorithms in classification task. PSO is a population-based optimization technique inspired by the social behavior of birds flocking or fish schooling.
## Overview
The PSOOptimizer class provided in this module allows users to optimize hyperparameters for four different types of machine learning algorithms:
* K-Nearest Neighbors (KNN)
* Random Forest (RF)
* Decision Tree (DT)
* Support Vector Classifier (SVC)
The optimization process aims to find the best set of hyperparameters that maximize the accuracy of the respective classifier on a given dataset.
## Requirements
- Python 3.x
- Required Python packages: numpy, joblib, scikit-learn, tqdm
Make sure to install these dependencies using pip before using the module.
## Usage
1. Install the `pso-optimizer` library:
```bash
pip install pso-optimizer
```
2. Example usage is in `main.py` file.
Files
* `main.py`: The main script to run PSO hyperparameter optimization.
* `pso_optimizer.py`: Contains the PSOOptimizer class for PSO optimization.
* `hyperparameter_mappings.py`: Contains mappings for hyperparameters used in different machine learning models.
* `README.md`: This file.
## Acknowledgments
The implementation of PSO hyperparameter optimization is inspired by the paper "The Particle Swarm — Explosion, Stability, and Convergence in a Multidimensional Complex Space" by Clerc and Kennedy.
## Citation
If you use this package in your work, please cite it using the following information:
@software{pso_optimizer,
author = {Mert Bayraktar},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/mBayraktar12/PSO-Hyperparameter-Selection/tree/main}},
version = {1.0.0}
}