<p align="center">
<img style="width:100%;" src="https://thieu1995.github.io/post/2023-08/metaperceptron1.png" alt="MetaPerceptron"/>
</p>
---
[](https://github.com/thieu1995/MetaPerceptron/releases)
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[](https://t.me/+fRVCJGuGJg1mNDg1)
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[](https://zenodo.org/doi/10.5281/zenodo.10251021)
[](https://www.gnu.org/licenses/gpl-3.0)
MetaPerceptron (Metaheuristic-optimized Multi-Layer Perceptron) is a Python library that implements variants and the
traditional version of Multi-Layer Perceptron models. These include Metaheuristic-optimized MLP models (GA, PSO, WOA, TLO, DE, ...)
and Gradient Descent-optimized MLP models (SGD, Adam, Adelta, Adagrad, ...). 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 features provided by the Scikit-Learn library.
* **Free software:** GNU General Public License (GPL) V3 license
* **Provided Estimator**: `MlpRegressor`, `MlpClassifier`, `MhaMlpRegressor`, `MhaMlpClassifier`
* **Provided Utility**: `MhaMlpTuner` and `MhaMlpComparator`
* **Total Metaheuristic-trained MLP Regressor**: > 200 Models
* **Total Metaheuristic-trained MLP Classifier**: > 200 Models
* **Total Gradient Descent-trained MLP Regressor**: 12 Models
* **Total Gradient Descent-trained MLP Classifier**: 12 Models
* **Supported performance metrics**: >= 67 (47 regressions and 20 classifications)
* **Documentation:** https://metaperceptron.readthedocs.io
* **Python versions:** >= 3.8.x
* **Dependencies:** numpy, scipy, scikit-learn, torch, mealpy, pandas, permetrics.
# 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](https://doi.org/10.1016/j.ins.2014.01.038)
Please include these citations if you plan to use this library:
```code
@software{nguyen_van_thieu_2023_10251022,
author = {Nguyen Van Thieu},
title = {MetaPerceptron: A Standardized Framework for Metaheuristic-Trained Multi-Layer Perceptron},
month = dec,
year = 2023,
publisher = {Zenodo},
doi = {10.5281/zenodo.10251021},
url = {https://github.com/thieu1995/MetaPerceptron}
}
@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}
}
```
# Simple Tutorial
* Install the [current PyPI release](https://pypi.python.org/pypi/metaperceptron):
```sh
$ pip install metaperceptron==2.0.0
```
* Check the version:
```sh
$ python
>>> import metaperceptron
>>> metaperceptron.__version__
```
* Import all provided classes from MetaPerceptron
```python
from metaperceptron import DataTransformer, Data
from metaperceptron import MhaMlpRegressor, MhaMlpClassifier, MlpRegressor, MlpClassifier
from metaperceptron import MhaMlpTuner, MhaMlpComparator
```
* In this tutorial, we will use Genetic Algorithm to train Multi-Layer Perceptron network for classification task.
For more complex examples and use cases, please check the folder [examples](examples).
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from metaperceptron import DataTransformer, MhaMlpClassifier
## Load the dataset
X, y = load_iris(return_X_y=True)
## Split train and test
X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.2)
## Scale dataset with two methods: standard and minmax
dt = DataTransformer(scaling_methods=("standard", "minmax"))
X_train_scaled = dt.fit_transform(X_train)
X_test_scaled = dt.transform(X_test)
## Define Genetic Algorithm-trained Multi-Layer Perceptron
opt_paras = {"epoch": 100, "pop_size": 20}
model = MhaMlpClassifier(hidden_layers=(50,), act_names="Tanh", dropout_rates=None, act_output=None,
optim="BaseGA", optim_paras=opt_paras, obj_name="F1S", seed=42, verbose=True)
## Train the model
model.fit(X=X_train_scaled, y=y_train)
## Test the model
y_pred = model.predict(X_test)
print(y_pred)
## Print the score
print(model.score(X_test_scaled, y_test))
## Calculate some metrics
print(model.evaluate(y_true=y_test, y_pred=y_pred, list_metrics=["AS", "PS", "RS", "F2S", "CKS", "FBS"]))
```
# 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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"description": "\n<p align=\"center\">\n<img style=\"width:100%;\" src=\"https://thieu1995.github.io/post/2023-08/metaperceptron1.png\" alt=\"MetaPerceptron\"/>\n</p>\n\n\n---\n\n[](https://github.com/thieu1995/MetaPerceptron/releases)\n[](https://pypi.python.org/pypi/metaperceptron) \n[](https://badge.fury.io/py/metaperceptron)\n\n\n\n[](https://pepy.tech/project/metaperceptron)\n[](https://github.com/thieu1995/metaperceptron/actions/workflows/publish-package.yaml)\n\n[](https://metaperceptron.readthedocs.io/en/latest/?badge=latest)\n[](https://t.me/+fRVCJGuGJg1mNDg1)\n\n[](https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project)\n[](https://zenodo.org/doi/10.5281/zenodo.10251021)\n[](https://www.gnu.org/licenses/gpl-3.0)\n\n\nMetaPerceptron (Metaheuristic-optimized Multi-Layer Perceptron) is a Python library that implements variants and the \ntraditional version of Multi-Layer Perceptron models. These include Metaheuristic-optimized MLP models (GA, PSO, WOA, TLO, DE, ...) \nand Gradient Descent-optimized MLP models (SGD, Adam, Adelta, Adagrad, ...). It provides a comprehensive list of \noptimizers for training MLP models and is also compatible with the Scikit-Learn library. With MetaPerceptron, \nyou can perform searches and hyperparameter tuning using the features provided by the Scikit-Learn library.\n\n* **Free software:** GNU General Public License (GPL) V3 license\n* **Provided Estimator**: `MlpRegressor`, `MlpClassifier`, `MhaMlpRegressor`, `MhaMlpClassifier`\n* **Provided Utility**: `MhaMlpTuner` and `MhaMlpComparator` \n* **Total Metaheuristic-trained MLP Regressor**: > 200 Models \n* **Total Metaheuristic-trained MLP Classifier**: > 200 Models\n* **Total Gradient Descent-trained MLP Regressor**: 12 Models\n* **Total Gradient Descent-trained MLP Classifier**: 12 Models\n* **Supported performance metrics**: >= 67 (47 regressions and 20 classifications)\n* **Documentation:** https://metaperceptron.readthedocs.io\n* **Python versions:** >= 3.8.x\n* **Dependencies:** numpy, scipy, scikit-learn, torch, mealpy, pandas, permetrics. \n\n\n# Citation Request \n\nIf you want to understand how Metaheuristic is applied to Multi-Layer Perceptron, you need to read the paper \ntitled **\"Let a biogeography-based optimizer train your Multi-Layer Perceptron\"**. \nThe paper can be accessed at the following [link](https://doi.org/10.1016/j.ins.2014.01.038)\n\n\nPlease include these citations if you plan to use this library:\n\n```code\n\n@software{nguyen_van_thieu_2023_10251022,\n author = {Nguyen Van Thieu},\n title = {MetaPerceptron: A Standardized Framework for Metaheuristic-Trained Multi-Layer Perceptron},\n month = dec,\n year = 2023,\n publisher = {Zenodo},\n doi = {10.5281/zenodo.10251021},\n url = {https://github.com/thieu1995/MetaPerceptron}\n}\n\n@article{van2023mealpy,\n title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},\n author={Van Thieu, Nguyen and Mirjalili, Seyedali},\n journal={Journal of Systems Architecture},\n year={2023},\n publisher={Elsevier},\n doi={10.1016/j.sysarc.2023.102871}\n}\n\n@article{van2023groundwater,\n title={Groundwater level modeling using Augmented Artificial Ecosystem Optimization},\n author={Van Thieu, Nguyen and Barma, Surajit Deb and Van Lam, To and Kisi, Ozgur and Mahesha, Amai},\n journal={Journal of Hydrology},\n volume={617},\n pages={129034},\n year={2023},\n publisher={Elsevier}\n}\n\n@article{thieu2019efficient,\n title={Efficient time-series forecasting using neural network and opposition-based coral reefs optimization},\n author={Thieu Nguyen, Tu Nguyen and Nguyen, Binh Minh and Nguyen, Giang},\n journal={International Journal of Computational Intelligence Systems},\n volume={12},\n number={2},\n pages={1144--1161},\n year={2019}\n}\n\n```\n\n# Simple Tutorial\n\n* Install the [current PyPI release](https://pypi.python.org/pypi/metaperceptron):\n```sh\n$ pip install metaperceptron==2.0.0\n```\n\n* Check the version:\n\n```sh\n$ python\n>>> import metaperceptron\n>>> metaperceptron.__version__\n```\n\n* Import all provided classes from MetaPerceptron\n\n```python\nfrom metaperceptron import DataTransformer, Data\nfrom metaperceptron import MhaMlpRegressor, MhaMlpClassifier, MlpRegressor, MlpClassifier\nfrom metaperceptron import MhaMlpTuner, MhaMlpComparator\n```\n\n* In this tutorial, we will use Genetic Algorithm to train Multi-Layer Perceptron network for classification task.\nFor more complex examples and use cases, please check the folder [examples](examples).\n\n```python\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom metaperceptron import DataTransformer, MhaMlpClassifier\n\n## Load the dataset\nX, y = load_iris(return_X_y=True)\n\n## Split train and test\nX_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.2)\n\n## Scale dataset with two methods: standard and minmax\ndt = DataTransformer(scaling_methods=(\"standard\", \"minmax\"))\nX_train_scaled = dt.fit_transform(X_train)\nX_test_scaled = dt.transform(X_test)\n\n## Define Genetic Algorithm-trained Multi-Layer Perceptron\nopt_paras = {\"epoch\": 100, \"pop_size\": 20}\nmodel = MhaMlpClassifier(hidden_layers=(50,), act_names=\"Tanh\", dropout_rates=None, act_output=None,\n optim=\"BaseGA\", optim_paras=opt_paras, obj_name=\"F1S\", seed=42, verbose=True)\n## Train the model\nmodel.fit(X=X_train_scaled, y=y_train)\n\n## Test the model\ny_pred = model.predict(X_test)\nprint(y_pred)\n\n## Print the score\nprint(model.score(X_test_scaled, y_test))\n\n## Calculate some metrics\nprint(model.evaluate(y_true=y_test, y_pred=y_pred, list_metrics=[\"AS\", \"PS\", \"RS\", \"F2S\", \"CKS\", \"FBS\"]))\n```\n\n\n# Support (questions, problems)\n\n### Official Links \n\n* Official source code repo: https://github.com/thieu1995/MetaPerceptron\n* Official document: https://metapeceptron.readthedocs.io/\n* Download releases: https://pypi.org/project/metaperceptron/\n* Issue tracker: https://github.com/thieu1995/MetaPerceptron/issues\n* Notable changes log: https://github.com/thieu1995/MetaPerceptron/blob/master/ChangeLog.md\n* Official chat group: 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