<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)
[](https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project)
[](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
* **Total Metaheuristic-based MLP Regressor**: > 200 Models
* **Total Metaheuristic-based MLP Classifier**: > 200 Models
* **Total Gradient Descent-based MLP Regressor**: 12 Models
* **Total Gradient Descent-based MLP Classifier**: 12 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](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: Unleashing the Power of Metaheuristic-optimized Multi-Layer Perceptron - A Python Library},
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}
}
```
# Installation
* Install the [current PyPI release](https://pypi.python.org/pypi/metaperceptron):
```sh
$ pip install metaperceptron==1.1.0
```
* Install directly from source code
```sh
$ 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:
```sh
$ pip install git+https://github.com/thieu1995/MetaPerceptron
```
After installation, you can import MetaPerceptron as any other Python module:
```sh
$ python
>>> import metaperceptron
>>> metaperceptron.__version__
```
### Examples
Please check all use cases and examples in folder [examples](examples).
1) MetaPerceptron provides this useful classes
```python
from metaperceptron import DataTransformer, Data
from metaperceptron import MlpRegressor, MlpClassifier
from metaperceptron import MhaMlpRegressor, MhaMlpClassifier
```
2) What you can do with `DataTransformer` class
We provide many scaler classes that you can select and make a combination of transforming your data via
DataTransformer class. For example:
2.1) I want to scale data by `Loge` and then `Sqrt` and then `MinMax`:
```python
from metaperceptron import DataTransformer
import pandas as pd
from sklearn.model_selection import train_test_split
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:5].values
y = dataset.iloc[:, 5].values
X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.2)
dt = DataTransformer(scaling_methods=("loge", "sqrt", "minmax"))
X_train_scaled = dt.fit_transform(X_train)
X_test_scaled = dt.transform(X_test)
```
2.2) I want to scale data by `YeoJohnson` and then `Standard`:
```python
from metaperceptron import DataTransformer
import pandas as pd
from sklearn.model_selection import train_test_split
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:5].values
y = dataset.iloc[:, 5].values
X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.2)
dt = DataTransformer(scaling_methods=("yeo-johnson", "standard"))
X_train_scaled = dt.fit_transform(X_train)
X_test_scaled = dt.transform(X_test)
```
3) What can you do with `Data` class
+ You can load your dataset into Data class
+ You can split dataset to train and test set
+ You can scale dataset without using DataTransformer class
+ You can scale labels using LabelEncoder
```python
from metaperceptron import Data
import pandas as pd
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:5].values
y = dataset.iloc[:, 5].values
data = Data(X, y, name="position_salaries")
#### Split dataset into train and test set
data.split_train_test(test_size=0.2, shuffle=True, random_state=100, inplace=True)
#### Feature Scaling
data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("standard", "sqrt", "minmax"))
data.X_test = scaler_X.transform(data.X_test)
data.y_train, scaler_y = data.encode_label(data.y_train) # This is for classification problem only
data.y_test = scaler_y.transform(data.y_test)
```
4) What can you do with all model classes
+ Define the model
+ Use provides functions to train, predict, and evaluate model
```python
from metaperceptron import MlpRegressor, MlpClassifier, MhaMlpRegressor, MhaMlpClassifier
## Use standard MLP model for regression problem
regressor = MlpRegressor(hidden_size=50, act1_name="tanh", act2_name="sigmoid", obj_name="MSE",
max_epochs=1000, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=False)
## Use standard MLP model for classification problem
classifier = MlpClassifier(hidden_size=50, act1_name="tanh", act2_name="sigmoid", obj_name="NLLL",
max_epochs=1000, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=False)
## Use Metaheuristic-optimized MLP model for regression problem
print(MhaMlpClassifier.SUPPORTED_OPTIMIZERS)
print(MhaMlpClassifier.SUPPORTED_REG_OBJECTIVES)
opt_paras = {"name": "WOA", "epoch": 100, "pop_size": 30}
regressor = MhaMlpRegressor(hidden_size=50, act1_name="tanh", act2_name="sigmoid",
obj_name="MSE", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True)
## Use Metaheuristic-optimized MLP model for classification problem
print(MhaMlpClassifier.SUPPORTED_OPTIMIZERS)
print(MhaMlpClassifier.SUPPORTED_CLS_OBJECTIVES)
opt_paras = {"name": "WOA", "epoch": 100, "pop_size": 30}
classifier = MhaMlpClassifier(hidden_size=50, act1_name="tanh", act2_name="softmax",
obj_name="CEL", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True)
```
5) What can you do with model object
```python
from metaperceptron import MlpRegressor, Data
data = Data() # Assumption that you have provide this object like above
model = MlpRegressor(hidden_size=50, act1_name="tanh", act2_name="sigmoid", obj_name="MSE",
max_epochs=1000, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=False)
## Train the model
model.fit(data.X_train, data.y_train)
## Predicting a new result
y_pred = model.predict(data.X_test)
## Calculate metrics using score or scores functions.
print(model.score(data.X_test, data.y_test, method="MAE"))
print(model.scores(data.X_test, data.y_test, list_methods=["MAPE", "NNSE", "KGE", "MASE", "R2", "R", "R2S"]))
## Calculate metrics using evaluate function
print(model.evaluate(data.y_test, y_pred, list_metrics=("MSE", "RMSE", "MAPE", "NSE")))
## Save performance metrics to csv file
model.save_evaluation_metrics(data.y_test, y_pred, list_metrics=("RMSE", "MAE"), save_path="history", filename="metrics.csv")
## Save training loss to csv file
model.save_training_loss(save_path="history", filename="loss.csv")
## Save predicted label
model.save_y_predicted(X=data.X_test, y_true=data.y_test, save_path="history", filename="y_predicted.csv")
## Save model
model.save_model(save_path="history", filename="traditional_mlp.pkl")
## Load model
trained_model = MlpRegressor.load_model(load_path="history", filename="traditional_mlp.pkl")
```
# 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* **Total Metaheuristic-based MLP Regressor**: > 200 Models \n* **Total Metaheuristic-based MLP Classifier**: > 200 Models\n* **Total Gradient Descent-based MLP Regressor**: 12 Models\n* **Total Gradient Descent-based MLP Classifier**: 12 Models\n* **Supported performance metrics**: >= 67 (47 regressions and 20 classifications)\n* **Supported objective functions (as fitness functions or loss functions)**: >= 67 (47 regressions and 20 classifications)\n* **Documentation:** https://metaperceptron.readthedocs.io\n* **Python versions:** >= 3.8.x\n* **Dependencies:** numpy, scipy, scikit-learn, pandas, mealpy, permetrics, torch, skorch\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: Unleashing the Power of Metaheuristic-optimized Multi-Layer Perceptron - A Python Library},\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# Installation\n\n* Install the [current PyPI release](https://pypi.python.org/pypi/metaperceptron):\n```sh \n$ pip install metaperceptron==1.1.0\n```\n\n* Install directly from source code\n```sh \n$ git clone https://github.com/thieu1995/MetaPerceptron.git\n$ cd MetaPerceptron\n$ python setup.py install\n```\n\n* In case, you want to install the development version from Github:\n```sh \n$ pip install git+https://github.com/thieu1995/MetaPerceptron \n```\n\nAfter installation, you can import MetaPerceptron as any other Python module:\n\n```sh\n$ python\n>>> import metaperceptron\n>>> metaperceptron.__version__\n```\n\n### Examples\n\nPlease check all use cases and examples in folder [examples](examples).\n\n1) MetaPerceptron provides this useful classes\n\n```python\nfrom metaperceptron import DataTransformer, Data\nfrom metaperceptron import MlpRegressor, MlpClassifier\nfrom metaperceptron import MhaMlpRegressor, MhaMlpClassifier\n```\n\n2) What you can do with `DataTransformer` class\n\nWe provide many scaler classes that you can select and make a combination of transforming your data via \nDataTransformer class. For example: \n\n2.1) I want to scale data by `Loge` and then `Sqrt` and then `MinMax`:\n\n```python\nfrom metaperceptron import DataTransformer\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\ndataset = pd.read_csv('Position_Salaries.csv')\nX = dataset.iloc[:, 1:5].values\ny = dataset.iloc[:, 5].values\nX_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.2)\n\ndt = DataTransformer(scaling_methods=(\"loge\", \"sqrt\", \"minmax\"))\nX_train_scaled = dt.fit_transform(X_train)\nX_test_scaled = dt.transform(X_test)\n```\n\n2.2) I want to scale data by `YeoJohnson` and then `Standard`:\n\n```python\nfrom metaperceptron import DataTransformer\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\ndataset = pd.read_csv('Position_Salaries.csv')\nX = dataset.iloc[:, 1:5].values\ny = dataset.iloc[:, 5].values\nX_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.2)\n\ndt = DataTransformer(scaling_methods=(\"yeo-johnson\", \"standard\"))\nX_train_scaled = dt.fit_transform(X_train)\nX_test_scaled = dt.transform(X_test)\n```\n\n3) What can you do with `Data` class\n+ You can load your dataset into Data class\n+ You can split dataset to train and test set\n+ You can scale dataset without using DataTransformer class\n+ You can scale labels using LabelEncoder\n\n```python\nfrom metaperceptron import Data\nimport pandas as pd\n\ndataset = pd.read_csv('Position_Salaries.csv')\nX = dataset.iloc[:, 1:5].values\ny = dataset.iloc[:, 5].values\n\ndata = Data(X, y, name=\"position_salaries\")\n\n#### Split dataset into train and test set\ndata.split_train_test(test_size=0.2, shuffle=True, random_state=100, inplace=True)\n\n#### Feature Scaling\ndata.X_train, scaler_X = data.scale(data.X_train, scaling_methods=(\"standard\", \"sqrt\", \"minmax\"))\ndata.X_test = scaler_X.transform(data.X_test)\n\ndata.y_train, scaler_y = data.encode_label(data.y_train) # This is for classification problem only\ndata.y_test = scaler_y.transform(data.y_test)\n```\n\n4) What can you do with all model classes\n+ Define the model \n+ Use provides functions to train, predict, and evaluate model\n\n```python\nfrom metaperceptron import MlpRegressor, MlpClassifier, MhaMlpRegressor, MhaMlpClassifier\n\n## Use standard MLP model for regression problem\nregressor = MlpRegressor(hidden_size=50, act1_name=\"tanh\", act2_name=\"sigmoid\", obj_name=\"MSE\",\n max_epochs=1000, batch_size=32, optimizer=\"SGD\", optimizer_paras=None, verbose=False)\n\n## Use standard MLP model for classification problem \nclassifier = MlpClassifier(hidden_size=50, act1_name=\"tanh\", act2_name=\"sigmoid\", obj_name=\"NLLL\",\n max_epochs=1000, batch_size=32, optimizer=\"SGD\", optimizer_paras=None, verbose=False)\n\n## Use Metaheuristic-optimized MLP model for regression problem\nprint(MhaMlpClassifier.SUPPORTED_OPTIMIZERS)\nprint(MhaMlpClassifier.SUPPORTED_REG_OBJECTIVES)\n\nopt_paras = {\"name\": \"WOA\", \"epoch\": 100, \"pop_size\": 30}\nregressor = MhaMlpRegressor(hidden_size=50, act1_name=\"tanh\", act2_name=\"sigmoid\",\n obj_name=\"MSE\", optimizer=\"OriginalWOA\", optimizer_paras=opt_paras, verbose=True)\n\n## Use Metaheuristic-optimized MLP model for classification problem\nprint(MhaMlpClassifier.SUPPORTED_OPTIMIZERS)\nprint(MhaMlpClassifier.SUPPORTED_CLS_OBJECTIVES)\n\nopt_paras = {\"name\": \"WOA\", \"epoch\": 100, \"pop_size\": 30}\nclassifier = MhaMlpClassifier(hidden_size=50, act1_name=\"tanh\", act2_name=\"softmax\",\n obj_name=\"CEL\", optimizer=\"OriginalWOA\", optimizer_paras=opt_paras, verbose=True)\n```\n\n5) What can you do with model object\n\n```python\nfrom metaperceptron import MlpRegressor, Data \n\ndata = Data() # Assumption that you have provide this object like above\n\nmodel = MlpRegressor(hidden_size=50, act1_name=\"tanh\", act2_name=\"sigmoid\", obj_name=\"MSE\",\n max_epochs=1000, batch_size=32, optimizer=\"SGD\", optimizer_paras=None, verbose=False)\n\n## Train the model\nmodel.fit(data.X_train, data.y_train)\n\n## Predicting a new result\ny_pred = model.predict(data.X_test)\n\n## Calculate metrics using score or scores functions.\nprint(model.score(data.X_test, data.y_test, method=\"MAE\"))\nprint(model.scores(data.X_test, data.y_test, list_methods=[\"MAPE\", \"NNSE\", \"KGE\", \"MASE\", \"R2\", \"R\", \"R2S\"]))\n\n## Calculate metrics using evaluate function\nprint(model.evaluate(data.y_test, y_pred, list_metrics=(\"MSE\", \"RMSE\", \"MAPE\", \"NSE\")))\n\n## Save performance metrics to csv file\nmodel.save_evaluation_metrics(data.y_test, y_pred, list_metrics=(\"RMSE\", \"MAE\"), save_path=\"history\", filename=\"metrics.csv\")\n\n## Save training loss to csv file\nmodel.save_training_loss(save_path=\"history\", filename=\"loss.csv\")\n\n## Save predicted label\nmodel.save_y_predicted(X=data.X_test, y_true=data.y_test, save_path=\"history\", filename=\"y_predicted.csv\")\n\n## Save model\nmodel.save_model(save_path=\"history\", filename=\"traditional_mlp.pkl\")\n\n## Load model \ntrained_model = MlpRegressor.load_model(load_path=\"history\", filename=\"traditional_mlp.pkl\")\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: https://t.me/+fRVCJGuGJg1mNDg1\n\n* This project also related to our another projects which are \"optimization\" and \"machine learning\", check it here:\n * https://github.com/thieu1995/mealpy\n * https://github.com/thieu1995/metaheuristics\n * https://github.com/thieu1995/opfunu\n * https://github.com/thieu1995/enoppy\n * https://github.com/thieu1995/permetrics\n * https://github.com/thieu1995/MetaCluster\n * https://github.com/thieu1995/pfevaluator\n * https://github.com/thieu1995/IntelELM\n * https://github.com/thieu1995/reflame\n * https://github.com/aiir-team\n",
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