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
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:
@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}
}
- Install the current PyPI release:
$ pip install metaperceptron==1.1.0- 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__Please check all use cases and examples in folder examples.
- MetaPerceptron provides this useful classes
from metaperceptron import DataTransformer, Data
from metaperceptron import MlpRegressor, MlpClassifier
from metaperceptron import MhaMlpRegressor, MhaMlpClassifier- What you can do with
DataTransformerclass
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:
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:
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)- What can you do with
Dataclass
- 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
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)- What can you do with all model classes
- Define the model
- Use provides functions to train, predict, and evaluate model
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)- What can you do with model object
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")-
Official source code repo: https://github.com/thieu1995/MetaPerceptron
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Official document: https://metapeceptron.readthedocs.io/
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Download releases: https://pypi.org/project/metaperceptron/
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Issue tracker: https://github.com/thieu1995/MetaPerceptron/issues
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Notable changes log: https://github.com/thieu1995/MetaPerceptron/blob/master/ChangeLog.md
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Official chat group: https://t.me/+fRVCJGuGJg1mNDg1
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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