GBNN is a python library for dealing with classification (Binary and multi-class) and regression problems.
Gradient Boosted Neural Network
If you use this package, please cite, or if you are using GBNN in your paper, please cite our work GBNN.
@ARTICLE{10110967,
author={Emami, Seyedsaman and Martýnez-Muñoz, Gonzalo},
journal={IEEE Access},
title={Sequential Training of Neural Networks With Gradient Boosting},
year={2023},
volume={11},
pages={42738-42750},
doi={10.1109/ACCESS.2023.3271515}}
Sequential Training of Neural Networks with Gradient Boosting
The package is licensed under the GNU Lesser General Public License v2.1.
inbuilt Python package management system, pip. You can can install, update, or delete the GBNN.
pip install gbnnIf you want to use this library easily after installing it, you could import it into your python project. You can use this package with the standards of Scikit-learn. Note that this project can run on both Windows and Linux and is tested on both of them.
In the following, one can see the example of implementing the algorithm.
import gbnn
model = gbnn.GNEGNEClassifier(total_nn=200, num_nn_step=1, eta=1.0, solver='lbfgs',
subsample=0.5, tol=0.0, max_iter=200, random_state=None, activation='logistic')
model.fit(x_train, y_train)
model.predict(x_test)The default values of the GBNN's hyper-parameters are, as above code.
The total_nn applies to the number of hidden units. The total_nn regards the units per iteration.
And activation introduces the default activation function of the base neural network.
Check the example to see the GBNN performance over the classification and regression problem.
To implement the GBNN method through the cross-validation processes with K folds, you could also consider the cross_validation.py and imported as following. With this file, you would also take advantage of the Grid Search method in order to select the optimized hyper-parameter.
from gbnn import cross_validation, GNEGNEClassifier
model = GNEGNEClassifier()
param_grid = {'clf__num_nn_step': [1, 2, 3, 4], 'clf__subsample': [
0.25, 0.5, 0.75, 1], 'clf__eta': [0.025, 0.05, 0.1, 0.5, 1]}
cross_validation.gridsearch(X, y, model, param_grid, scoring_functions,
pipeline, best_scoring, random_state, n_cv_general, n_cv_intrain)the n_cv_general, refers to the number of cross-validation. the n_cv_intrain, refers to the number of within-train cross-validation.
This package takes advantage of the following libraries, which had already imported to the GBNN package.:
- numpy - Numerical Python
- pandas - python data analysis library
- scipy - Scientific computation in Python
- scikit-learn - Machine learning in Python
The developed multi-output regression with the related experiments is available in the GBNN-MO repository.
Contributions to the GBNN are welcome! . You can improve this project by creating an issue,
reporting an improvement or a bug, forking and creating a pull request to the
development branch. For more information, check the contributing guidelines.
The authors and developers involved in the development of the GBNN package can be found in the contributor's file.
Gradient Boosting, Neural Network
0.0.2
02 Feb 2022
26 Sep 2019