metaperceptron 1.1.0

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 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

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}
}

Installation

• 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__

Examples

Please check all use cases and examples in folder examples.

1. MetaPerceptron provides this useful classes

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:

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)

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

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

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

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