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https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms
artificial-neural-networks bayesian-optimization deep-learning genetic-algorithm grid-search hpo hyperband hyperparameter-optimization hyperparameter-tuning knn machine-learning machine-learning-algorithms optimization particle-swarm-optimization python-examples python-samples random-forest random-search svm tuning-parameters
Last synced: 2 days ago
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Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
- Host: GitHub
- URL: https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms
- Owner: LiYangHart
- License: mit
- Created: 2020-07-23T03:30:47.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-09-22T05:24:53.000Z (about 2 years ago)
- Last Synced: 2024-10-29T19:59:44.955Z (15 days ago)
- Topics: artificial-neural-networks, bayesian-optimization, deep-learning, genetic-algorithm, grid-search, hpo, hyperband, hyperparameter-optimization, hyperparameter-tuning, knn, machine-learning, machine-learning-algorithms, optimization, particle-swarm-optimization, python-examples, python-samples, random-forest, random-search, svm, tuning-parameters
- Language: Jupyter Notebook
- Homepage:
- Size: 235 KB
- Stars: 1,266
- Watchers: 88
- Forks: 300
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Hyperparameter Optimization of Machine Learning Algorithms
This code provides a hyper-parameter optimization implementation for machine learning algorithms, as described in the paper:
L. Yang and A. Shami, “[On hyperparameter optimization of machine learning algorithms: Theory and practice](https://arxiv.org/abs/2007.15745),” Neurocomputing, vol. 415, pp. 295–316, 2020, doi: https://doi.org/10.1016/j.neucom.2020.07.061.
To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model's performance. In this paper, optimizing the hyper-parameters of common machine learning models is studied. We introduce several state-of-the-art optimization techniques and discuss how to apply them to machine learning algorithms. Many available libraries and frameworks developed for hyper-parameter optimization problems are provided, and some open challenges of hyper-parameter optimization research are also discussed in this paper. Moreover, experiments are conducted on benchmark datasets to compare the performance of different optimization methods and provide practical examples of hyper-parameter optimization.
This paper and code will help industrial users, data analysts, and researchers to better develop machine learning models by identifying the proper hyper-parameter configurations effectively.
- PS: A comprehensive **Automated Machine Learning (AutoML)** tutorial code can be found in: [AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics](https://github.com/Western-OC2-Lab/AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics)
* Including **automated data pre-processing, automated feature engineering, automated model selection, hyperparameter optimization, and automated model updating** (concept drift adaptation).
## Paper
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice
[One-column version: arXiv](https://arxiv.org/abs/2007.15745)
[Two-column version: Elsevier](https://www.sciencedirect.com/science/article/pii/S0925231220311693)
### Quick Navigation
**Section 3**: Important hyper-parameters of common machine learning algorithms
**Section 4**: Hyper-parameter optimization techniques introduction
**Section 5**: How to choose optimization techniques for different machine learning models
**Section 6**: Common Python libraries/tools for hyper-parameter optimization
**Section 7**: Experimental results (sample code in "HPO_Regression.ipynb" and "HPO_Classification.ipynb")
**Section 8**: Open challenges and future research directions
**Summary table for Sections 3-6**: Table 2: A comprehensive overview of common ML models, their hyper-parameters, suitable optimization techniques, and available Python libraries
**Summary table for Sections 8**: Table 10: The open challenges and future directions of HPO research
## Implementation
Sample code for hyper-parameter optimization implementation for machine learning algorithms is provided in this repository.
### Sample code for Regression problems
[HPO_Regression.ipynb](https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms/blob/master/HPO_Regression.ipynb)
**Dataset used:** [Boston-Housing](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html)
### Sample code for Classification problems
[HPO_Classification.ipynb](https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms/blob/master/HPO_Classification.ipynb)
**Dataset used:** [MNIST](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
### Machine Learning & Deep Learning Algorithms
* Random forest (RF)
* Support vector machine (SVM)
* K-nearest neighbor (KNN)
* Artificial Neural Networks (ANN)
### Hyperparameter Configuration Space
| ML Model | Hyper-parameter | Type | Search Space |
|-----------------------|--------------------------|--------------------|---------------------------------------------|
| RF Classifier | n_estimators | Discrete | [10,100] |
| | max_depth | Discrete | [5,50] |
| | min_samples_split | Discrete | [2,11] |
| | min_samples_leaf | Discrete | [1,11] |
| | criterion | Categorical | 'gini', 'entropy' |
| | max_features | Discrete | [1,64] |
| SVM Classifier | C | Continuous | [0.1,50] |
| | kernel | Categorical | 'linear', 'poly', 'rbf', 'sigmoid' |
| KNN Classifier | n_neighbors | Discrete | [1,20] |
| ANN Classifier | optimizer | Categorical | 'adam', 'rmsprop', 'sgd' |
| | activation | Categorical | 'relu', 'tanh' |
| | batch_size | Discrete | [16,64] |
| | neurons | Discrete | [10,100] |
| | epochs | Discrete | [20,50] |
| | patience | Discrete | [3,20] |
| RF Regressor | n_estimators | Discrete | [10,100] |
| | max_depth | Discrete | [5,50] |
| | min_samples_split | Discrete | [2,11] |
| | min_samples_leaf | Discrete | [1,11] |
| | criterion | Categorical | 'mse', 'mae' |
| | max_features | Discrete | [1,13] |
| SVM Regressor | C | Continuous | [0.1,50] |
| | kernel | Categorical | 'linear', 'poly', 'rbf', 'sigmoid' |
| | epsilon | Continuous | [0.001,1] |
| KNN Regressor | n_neighbors | Discrete | [1,20] |
| ANN Regressor | optimizer | Categorical | 'adam', 'rmsprop' |
| | activation | Categorical | 'relu', 'tanh' |
| | loss | Categorical | 'mse', 'mae' |
| | batch_size | Discrete | [16,64] |
| | neurons | Discrete | [10,100] |
| | epochs | Discrete | [20,50] |
| | patience | Discrete | [3,20] |
### HPO Algorithms
* Grid search
* Random search
* Hyperband
* Bayesian Optimization with Gaussian Processes (BO-GP)
* Bayesian Optimization with Tree-structured Parzen Estimator (BO-TPE)
* Particle swarm optimization (PSO)
* Genetic algorithm (GA)
### Requirements
* Python 3.5+
* [Keras](https://keras.io/)
* [scikit-learn](https://scikit-learn.org/stable/)
* [hyperband](https://github.com/thuijskens/scikit-hyperband)
* [scikit-optimize](https://github.com/scikit-optimize/scikit-optimize)
* [hyperopt](https://github.com/hyperopt/hyperopt)
* [optunity](https://github.com/claesenm/optunity)
* [DEAP](https://github.com/rsteca/sklearn-deap)
* [TPOT](https://github.com/EpistasisLab/tpot)
## Contact-Info
Please feel free to contact me for any questions or cooperation opportunities. I'd be happy to help.
* Email: [[email protected]](mailto:[email protected])
* GitHub: [LiYangHart](https://github.com/LiYangHart) and [Western OC2 Lab](https://github.com/Western-OC2-Lab/)
* LinkedIn: [Li Yang](https://www.linkedin.com/in/li-yang-phd-65a190176/)
* Google Scholar: [Li Yang](https://scholar.google.com.eg/citations?user=XEfM7bIAAAAJ&hl=en) and [OC2 Lab](https://scholar.google.com.eg/citations?user=oiebNboAAAAJ&hl=en)
## Citation
If you find this repository useful in your research, please cite this article as:
L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” *Neurocomputing*, vol. 415, pp. 295–316, 2020, doi: https://doi.org/10.1016/j.neucom.2020.07.061.
```
@article{YANG2020295,
title = "On hyperparameter optimization of machine learning algorithms: Theory and practice",
author = "Li Yang and Abdallah Shami",
volume = "415",
pages = "295 - 316",
journal = "Neurocomputing",
year = "2020",
issn = "0925-2312",
doi = "https://doi.org/10.1016/j.neucom.2020.07.061",
url = "http://www.sciencedirect.com/science/article/pii/S0925231220311693"
}
```