metaperceptron 1.0.0

MetaPerceptron: Unleashing the Power of Metaheuristic-optimized Multi-Layer Perceptron - A Python Library

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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