graforvfl


Namegraforvfl JSON
Version 1.2.0 PyPI version JSON
download
home_pagehttps://github.com/thieu1995/GrafoRVFL
SummaryGrafoRVFL: A Gradient-Free Optimization Framework for Boosting Random Vector Functional Link Network
upload_time2024-11-13 01:53:32
maintainerNone
docs_urlNone
authorThieu
requires_python>=3.8
licenseGPLv3
keywords random vector functional link machine learning artificial intelligence deep learning neural networks single hidden layer network random projection rvfl feed-forward neural network artificial neural network classification regression supervised learning online learning generalization optimization algorithms kernel rvfl cross-validationgenetic algorithm (ga) particle swarm optimization (pso) ant colony optimization (aco) differential evolution (de) simulated annealing grey wolf optimizer (gwo) whale optimization algorithm (woa) confusion matrix recall precision accuracy pearson correlation coefficient (pcc) spearman correlation coefficient (scc) global optimization convergence analysis search space exploration local search computational intelligence robust optimization metaheuristic metaheuristic algorithms nature-inspired computing nature-inspired algorithms swarm-based computation metaheuristic-based rvfl gradient-free optimizationgradient-free optimized rvfl metaheuristic-optimized rvfl performance analysis intelligent optimization simulations
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requirements No requirements were recorded.
Travis-CI No Travis.
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# GrafoRVFL (GRAdient Free Optimized Random Vector Functional Link)

---

[![GitHub release](https://img.shields.io/badge/release-1.2.0-yellow.svg)](https://github.com/thieu1995/GrafoRVFL/releases)
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GrafoRVFL is an open-source library in Python that employs gradient-free optimization (GA, PSO, WOA, TLO, DE, ...) to 
optimize Random Vector Functional Link Networks. It is entirely implemented based on Numpy and fully compatible 
with the interfaces of the Scikit-Learn library. With GrafoRVFL, you can fine-tune the hyper-parameters of network in the network using gradient-free optimizers.


* **Free software:** GNU General Public License (GPL) V3 license
* **Documentation:** https://graforvfl.readthedocs.io
* **Provided Estimator**: `RvflRegressor`, `RvflClassifier`, `GfoRvflTuner`
* **Python versions:** >= 3.8.x
* **Dependencies:** numpy, scipy, scikit-learn, pandas, mealpy, permetrics


# Citation Request 

* Learn more about Random Vector Functional Link from [this paper](https://doi.org/10.1016/j.ins.2015.09.025)

* Learn more about on how to use Gradient Free Optimization to fine-tune the hyper-parameter of RVFL networks from 
[this paper](https://doi.org/10.1016/j.neucom.2018.07.080)


Please include these citations if you plan to use this library:

```bibtex

@software{nguyen_van_thieu_2023_10258280,
  author       = {Nguyen Van Thieu},
  title        = {GrafoRVFL: A Gradient-Free Optimization Framework for Boosting Random Vector Functional Link Network},
  month        = dec,
  year         = 2023,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.10258280},
  url          = {https://github.com/thieu1995/GrafoRVFL}
}

@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}
}

@inproceedings{nguyen2019building,
  title={Building resource auto-scaler with functional-link neural network and adaptive bacterial foraging optimization},
  author={Nguyen, Thieu and Nguyen, Binh Minh and Nguyen, Giang},
  booktitle={International Conference on Theory and Applications of Models of Computation},
  pages={501--517},
  year={2019},
  organization={Springer}
}

@inproceedings{nguyen2018resource,
  title={A resource usage prediction system using functional-link and genetic algorithm neural network for multivariate cloud metrics},
  author={Nguyen, Thieu and Tran, Nhuan and Nguyen, Binh Minh and Nguyen, Giang},
  booktitle={2018 IEEE 11th conference on service-oriented computing and applications (SOCA)},
  pages={49--56},
  year={2018},
  organization={IEEE},
  doi={10.1109/SOCA.2018.00014}
}

```

# Installation

* Install the [current PyPI release](https://pypi.python.org/pypi/graforvfl):
```sh 
$ pip install graforvfl
```

After installation, you can check the installed version by:

```sh
$ python
>>> import graforvfl
>>> graforvfl.__version__
```

# Example

Below is the example code of how to use Gradient Free Optimization to tune hyper-parameter of RVFL network.
The more complicated cases in the folder: [examples](/examples). You can also read the [documentation](https://graforvfl.readthedocs.io/) 
for more detailed installation instructions, explanations, and examples.

```python
from sklearn.datasets import load_breast_cancer
from mealpy import StringVar, IntegerVar
from graforvfl import Data, GfoRvflTuner

## Load data object
X, y = load_breast_cancer(return_X_y=True)
data = Data(X, y)

## Split train and test
data.split_train_test(test_size=0.2, random_state=2, inplace=True)
print(data.X_train.shape, data.X_test.shape)

## Scaling dataset
data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("standard", "minmax"))
data.X_test = scaler_X.transform(data.X_test)

data.y_train, scaler_y = data.encode_label(data.y_train)
data.y_test = scaler_y.transform(data.y_test)

# Design the boundary (parameters)
my_bounds = [
    IntegerVar(lb=2, ub=1000, name="size_hidden"),
    StringVar(valid_sets=("none", "relu", "leaky_relu", "celu", "prelu", "gelu",
                          "elu", "selu", "rrelu", "tanh", "sigmoid"), name="act_name"),
    StringVar(valid_sets=("orthogonal", "he_uniform", "he_normal", "glorot_uniform", "glorot_normal",
                          "lecun_uniform", "lecun_normal", "random_uniform", "random_normal"), name="weight_initializer")
]

opt_paras = {"name": "WOA", "epoch": 10, "pop_size": 20}
model = GfoRvflTuner(problem_type="classification", bounds=my_bounds, cv=3, scoring="AS",
                      optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True, seed=42)
model.fit(data.X_train, data.y_train)
print(model.best_params)
print(model.best_estimator)
print(model.best_estimator.scores(data.X_test, data.y_test, list_metrics=("PS", "RS", "NPV", "F1S", "F2S")))
```

# Official channels 

* [Official source code repository](https://github.com/thieu1995/GrafoRVFL)
* [Official document](https://graforvfl.readthedocs.io/)
* [Download releases](https://pypi.org/project/graforvfl/) 
* [Issue tracker](https://github.com/thieu1995/GrafoRVFL/issues) 
* [Notable changes log](/ChangeLog.md)
* [Official discussion group](https://t.me/+fRVCJGuGJg1mNDg1)

---

Developed by: [Thieu](mailto:nguyenthieu2102@gmail.com?Subject=GrafoRVFL_QUESTIONS) @ 2023

            

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    "description": "\n# GrafoRVFL (GRAdient Free Optimized Random Vector Functional Link)\n\n---\n\n[![GitHub release](https://img.shields.io/badge/release-1.2.0-yellow.svg)](https://github.com/thieu1995/GrafoRVFL/releases)\n[![Wheel](https://img.shields.io/pypi/wheel/gensim.svg)](https://pypi.python.org/pypi/graforvfl) \n[![PyPI version](https://badge.fury.io/py/graforvfl.svg)](https://badge.fury.io/py/graforvfl)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/graforvfl.svg)\n![PyPI - Status](https://img.shields.io/pypi/status/graforvfl.svg)\n![PyPI - Downloads](https://img.shields.io/pypi/dm/graforvfl.svg)\n[![Downloads](https://pepy.tech/badge/graforvfl)](https://pepy.tech/project/graforvfl)\n[![Tests & Publishes to PyPI](https://github.com/thieu1995/graforvfl/actions/workflows/publish-package.yaml/badge.svg)](https://github.com/thieu1995/graforvfl/actions/workflows/publish-package.yaml)\n![GitHub Release Date](https://img.shields.io/github/release-date/thieu1995/graforvfl.svg)\n[![Documentation Status](https://readthedocs.org/projects/graforvfl/badge/?version=latest)](https://graforvfl.readthedocs.io/en/latest/?badge=latest)\n[![Chat](https://img.shields.io/badge/Chat-on%20Telegram-blue)](https://t.me/+fRVCJGuGJg1mNDg1)\n![GitHub contributors](https://img.shields.io/github/contributors/thieu1995/graforvfl.svg)\n[![GitTutorial](https://img.shields.io/badge/PR-Welcome-%23FF8300.svg?)](https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10258280.svg)](https://doi.org/10.5281/zenodo.10258280)\n[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)\n\n\nGrafoRVFL is an open-source library in Python that employs gradient-free optimization (GA, PSO, WOA, TLO, DE, ...) to \noptimize Random Vector Functional Link Networks. It is entirely implemented based on Numpy and fully compatible \nwith the interfaces of the Scikit-Learn library. With GrafoRVFL, you can fine-tune the hyper-parameters of network in the network using gradient-free optimizers.\n\n\n* **Free software:** GNU General Public License (GPL) V3 license\n* **Documentation:** https://graforvfl.readthedocs.io\n* **Provided Estimator**: `RvflRegressor`, `RvflClassifier`, `GfoRvflTuner`\n* **Python versions:** >= 3.8.x\n* **Dependencies:** numpy, scipy, scikit-learn, pandas, mealpy, permetrics\n\n\n# Citation Request \n\n* Learn more about Random Vector Functional Link from [this paper](https://doi.org/10.1016/j.ins.2015.09.025)\n\n* Learn more about on how to use Gradient Free Optimization to fine-tune the hyper-parameter of RVFL networks from \n[this paper](https://doi.org/10.1016/j.neucom.2018.07.080)\n\n\nPlease include these citations if you plan to use this library:\n\n```bibtex\n\n@software{nguyen_van_thieu_2023_10258280,\n  author       = {Nguyen Van Thieu},\n  title        = {GrafoRVFL: A Gradient-Free Optimization Framework for Boosting Random Vector Functional Link Network},\n  month        = dec,\n  year         = 2023,\n  publisher    = {Zenodo},\n  doi          = {10.5281/zenodo.10258280},\n  url          = {https://github.com/thieu1995/GrafoRVFL}\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@inproceedings{nguyen2019building,\n  title={Building resource auto-scaler with functional-link neural network and adaptive bacterial foraging optimization},\n  author={Nguyen, Thieu and Nguyen, Binh Minh and Nguyen, Giang},\n  booktitle={International Conference on Theory and Applications of Models of Computation},\n  pages={501--517},\n  year={2019},\n  organization={Springer}\n}\n\n@inproceedings{nguyen2018resource,\n  title={A resource usage prediction system using functional-link and genetic algorithm neural network for multivariate cloud metrics},\n  author={Nguyen, Thieu and Tran, Nhuan and Nguyen, Binh Minh and Nguyen, Giang},\n  booktitle={2018 IEEE 11th conference on service-oriented computing and applications (SOCA)},\n  pages={49--56},\n  year={2018},\n  organization={IEEE},\n  doi={10.1109/SOCA.2018.00014}\n}\n\n```\n\n# Installation\n\n* Install the [current PyPI release](https://pypi.python.org/pypi/graforvfl):\n```sh \n$ pip install graforvfl\n```\n\nAfter installation, you can check the installed version by:\n\n```sh\n$ python\n>>> import graforvfl\n>>> graforvfl.__version__\n```\n\n# Example\n\nBelow is the example code of how to use Gradient Free Optimization to tune hyper-parameter of RVFL network.\nThe more complicated cases in the folder: [examples](/examples). You can also read the [documentation](https://graforvfl.readthedocs.io/) \nfor more detailed installation instructions, explanations, and examples.\n\n```python\nfrom sklearn.datasets import load_breast_cancer\nfrom mealpy import StringVar, IntegerVar\nfrom graforvfl import Data, GfoRvflTuner\n\n## Load data object\nX, y = load_breast_cancer(return_X_y=True)\ndata = Data(X, y)\n\n## Split train and test\ndata.split_train_test(test_size=0.2, random_state=2, inplace=True)\nprint(data.X_train.shape, data.X_test.shape)\n\n## Scaling dataset\ndata.X_train, scaler_X = data.scale(data.X_train, scaling_methods=(\"standard\", \"minmax\"))\ndata.X_test = scaler_X.transform(data.X_test)\n\ndata.y_train, scaler_y = data.encode_label(data.y_train)\ndata.y_test = scaler_y.transform(data.y_test)\n\n# Design the boundary (parameters)\nmy_bounds = [\n    IntegerVar(lb=2, ub=1000, name=\"size_hidden\"),\n    StringVar(valid_sets=(\"none\", \"relu\", \"leaky_relu\", \"celu\", \"prelu\", \"gelu\",\n                          \"elu\", \"selu\", \"rrelu\", \"tanh\", \"sigmoid\"), name=\"act_name\"),\n    StringVar(valid_sets=(\"orthogonal\", \"he_uniform\", \"he_normal\", \"glorot_uniform\", \"glorot_normal\",\n                          \"lecun_uniform\", \"lecun_normal\", \"random_uniform\", \"random_normal\"), name=\"weight_initializer\")\n]\n\nopt_paras = {\"name\": \"WOA\", \"epoch\": 10, \"pop_size\": 20}\nmodel = GfoRvflTuner(problem_type=\"classification\", bounds=my_bounds, cv=3, scoring=\"AS\",\n                      optimizer=\"OriginalWOA\", optimizer_paras=opt_paras, verbose=True, seed=42)\nmodel.fit(data.X_train, data.y_train)\nprint(model.best_params)\nprint(model.best_estimator)\nprint(model.best_estimator.scores(data.X_test, data.y_test, list_metrics=(\"PS\", \"RS\", \"NPV\", \"F1S\", \"F2S\")))\n```\n\n# Official channels \n\n* [Official source code repository](https://github.com/thieu1995/GrafoRVFL)\n* [Official document](https://graforvfl.readthedocs.io/)\n* [Download releases](https://pypi.org/project/graforvfl/) \n* [Issue tracker](https://github.com/thieu1995/GrafoRVFL/issues) \n* [Notable changes log](/ChangeLog.md)\n* [Official discussion group](https://t.me/+fRVCJGuGJg1mNDg1)\n\n---\n\nDeveloped by: [Thieu](mailto:nguyenthieu2102@gmail.com?Subject=GrafoRVFL_QUESTIONS) @ 2023\n",
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