MetaPerceptron: Unleashing the Power of Metaheuristic-optimized Multi-Layer Perceptron - A Python Library
Project description
MetaPerceptron (Metaheuristic-optimized Multi-Layer Perceptron) is a Python library that implement the traditional MLP models that trained by Gradient Descent-based optimizers (SGD, Adam, Adelta, Adagrad,...), and Metaheuristic-optimized MLP models. It provides a comprehensive list of optimizers for training MLP models and is also compatible with the Scikit-Learn library. With MetaPerceptron, you can perform searches and hyperparameter tuning using the functionalities provided by the Scikit-Learn library.
- Free software: GNU General Public License (GPL) V3 license
- Provided Estimator: MlpRegressor, MlpClassifier, MhaMlpRegressor, MhaMlpClassifier
- Total Metaheuristic-based Mlp Regression: > 200 Models
- Total Metaheuristic-based Mlp Classification: > 200 Models
- Supported performance metrics: >= 67 (47 regressions and 20 classifications)
- Supported objective functions (as fitness functions or loss functions): >= 67 (47 regressions and 20 classifications)
- Documentation: https://metaperceptron.readthedocs.io
- Python versions: >= 3.8.x
- Dependencies: numpy, scipy, scikit-learn, pandas, mealpy, permetrics, torch, skorch
Citation Request
If you want to understand how Metaheuristic is applied to Multi-Layer Perceptron, you need to read the paper titled "Let a biogeography-based optimizer train your Multi-Layer Perceptron". The paper can be accessed at the following link
Please include these citations if you plan to use this library:
@article{van2023mealpy,
title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
author={Van Thieu, Nguyen and Mirjalili, Seyedali},
journal={Journal of Systems Architecture},
year={2023},
publisher={Elsevier},
doi={10.1016/j.sysarc.2023.102871}
}
@article{van2023groundwater,
title={Groundwater level modeling using Augmented Artificial Ecosystem Optimization},
author={Van Thieu, Nguyen and Barma, Surajit Deb and Van Lam, To and Kisi, Ozgur and Mahesha, Amai},
journal={Journal of Hydrology},
volume={617},
pages={129034},
year={2023},
publisher={Elsevier}
}
@article{thieu2019efficient,
title={Efficient time-series forecasting using neural network and opposition-based coral reefs optimization},
author={Thieu Nguyen, Tu Nguyen and Nguyen, Binh Minh and Nguyen, Giang},
journal={International Journal of Computational Intelligence Systems},
volume={12},
number={2},
pages={1144--1161},
year={2019}
}
Installation
- Install the current PyPI release:
$ pip install metaperceptron==1.0.0
- Install directly from source code
$ git clone https://github.com/thieu1995/MetaPerceptron.git
$ cd MetaPerceptron
$ python setup.py install
- In case, you want to install the development version from Github:
$ pip install git+https://github.com/thieu1995/MetaPerceptron
After installation, you can import MetaPerceptron as any other Python module:
$ python
>>> import metaperceptron
>>> metaperceptron.__version__
Support (questions, problems)
Official Links
-
Official source code repo: https://github.com/thieu1995/MetaPerceptron
-
Official document: https://metapeceptron.readthedocs.io/
-
Download releases: https://pypi.org/project/metaperceptron/
-
Issue tracker: https://github.com/thieu1995/MetaPerceptron/issues
-
Notable changes log: https://github.com/thieu1995/MetaPerceptron/blob/master/ChangeLog.md
-
Official chat group: https://t.me/+fRVCJGuGJg1mNDg1
-
This project also related to our another projects which are "optimization" and "machine learning", check it here:
- https://github.com/thieu1995/mealpy
- https://github.com/thieu1995/metaheuristics
- https://github.com/thieu1995/opfunu
- https://github.com/thieu1995/enoppy
- https://github.com/thieu1995/permetrics
- https://github.com/thieu1995/MetaCluster
- https://github.com/thieu1995/pfevaluator
- https://github.com/thieu1995/IntelELM
- https://github.com/thieu1995/reflame
- https://github.com/aiir-team
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