Metadata-Version: 2.1
Name: graforvfl
Version: 1.1.0
Summary: GrafoRVFL: A Gradient-Free Optimization Framework for Boosting Random Vector Functional Link Network
Home-page: https://github.com/thieu1995/GrafoRVFL
Author: Thieu
Author-email: nguyenthieu2102@gmail.com
License: GPLv3
Project-URL: Documentation, https://graforvfl.readthedocs.io/
Project-URL: Source Code, https://github.com/thieu1995/GrafoRVFL
Project-URL: Bug Tracker, https://github.com/thieu1995/GrafoRVFL/issues
Project-URL: Change Log, https://github.com/thieu1995/GrafoRVFL/blob/main/ChangeLog.md
Project-URL: Forum, https://t.me/+fRVCJGuGJg1mNDg1
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
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: System :: Benchmark
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Software Development :: Build Tools
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.17.1
Requires-Dist: scipy>=1.7.1
Requires-Dist: scikit-learn>=1.0.2
Requires-Dist: pandas>=1.3.5
Requires-Dist: mealpy>=3.0.1
Requires-Dist: permetrics>=2.0.0
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: pytest-cov==4.0.0; extra == "dev"
Requires-Dist: flake8>=4.0.1; extra == "dev"


# GrafoRVFL (GRAdient Free Optimized Random Vector Functional Link)

---

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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 
or optimize weights 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/10.1109/TCSS.2022.3146974)


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_methods=("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
