https://github.com/techtonique/nnetsauce
Statistical/Machine Learning using Randomized and Quasi-Randomized (neural) networks (currently Python & R)
https://github.com/techtonique/nnetsauce
deep-learning machine-learning neural-networks quasi-randomized statistical-learning
Last synced: 4 months ago
JSON representation
Statistical/Machine Learning using Randomized and Quasi-Randomized (neural) networks (currently Python & R)
- Host: GitHub
- URL: https://github.com/techtonique/nnetsauce
- Owner: Techtonique
- License: bsd-3-clause-clear
- Created: 2020-08-14T04:48:27.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2025-10-08T06:35:21.000Z (5 months ago)
- Last Synced: 2025-10-21T19:51:38.722Z (4 months ago)
- Topics: deep-learning, machine-learning, neural-networks, quasi-randomized, statistical-learning
- Language: Jupyter Notebook
- Homepage: https://techtonique.github.io/nnetsauce/
- Size: 32.2 MB
- Stars: 20
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
nnetsauce
--------
Randomized and Quasi-Randomized (neural) networks.
 [](https://github.com/thierrymoudiki/nnetsauce/blob/master/LICENSE) [](https://pepy.tech/project/nnetsauce)
[](https://anaconda.org/conda-forge/nnetsauce)
[](http://hits.dwyl.com/Techtonique/nnetsauce)
[](https://www.codefactor.io/repository/github/techtonique/nnetsauce)
[](https://techtonique.github.io/nnetsauce/)
## Contents
[Installing for Python and R](#installing-for-Python-and-R) |
[Package description](#package-description) |
[Quick start](#quick-start) |
[Contributing](#Contributing) |
[Tests](#Tests) |
[Dependencies](#dependencies) |
[Citing `nnetsauce`](#Citation) |
[API Documentation](#api-documentation) |
[References](#References) |
[License](#License)
## Installing (for Python and R)
### Python
- __1st method__: by using `pip` at the command line for the stable version
```bash
pip install nnetsauce
```
- __2nd method__: using `conda` (Linux and macOS only for now)
```bash
conda install -c conda-forge nnetsauce
```
(Note to self or developers: https://github.com/conda-forge/nnetsauce-feedstock and https://conda-forge.org/docs/maintainer/adding_pkgs.html#step-by-step-instructions and https://packaging.python.org/en/latest/guides/publishing-package-distribution-releases-using-github-actions-ci-cd-workflows/#the-whole-ci-cd-workflow)
- __3rd method__: from Github, for the development version
```bash
pip install git+https://github.com/Techtonique/nnetsauce.git
```
or
```bash
git clone https://github.com/Techtonique/nnetsauce.git
cd nnetsauce
make install
```
### R
**From GitHub**
```bash
remotes::install_github("Techtonique/nnetsauce_r") # the repo is in this organization
```
**From R-universe**
```bash
install.packages('nnetsauce', repos = c('https://techtonique.r-universe.dev',
'https://cloud.r-project.org'))
```
__General rule for using the package in R__: object accesses with `.`'s are replaced by `$`'s. R Examples can be found in the package, once installed, by typing (in R console):
```R
?nnetsauce::MultitaskClassifier
```
For a list of available models, visit [https://techtonique.github.io/nnetsauce/](https://techtonique.github.io/nnetsauce/).
## Package description
A package for Statistical/Machine Learning using Randomized and Quasi-Randomized (neural) networks. See next section.
## Quick start
There are multiple [examples here on GitHub](https://github.com/Techtonique/nnetsauce/tree/master/examples), plus [notebooks](https://github.com/Techtonique/nnetsauce/tree/master/nnetsauce/demo) (including R Markdown notebooks).
You can also read these [blog posts](https://thierrymoudiki.github.io/blog/#QuasiRandomizedNN).
_Lazy Deep (quasi-randomized neural) networks example_
```python
!pip install nnetsauce --upgrade
```
```python
import os
import nnetsauce as ns
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from time import time
data = load_breast_cancer()
X = data.data
y= data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123)
clf = ns.LazyDeepClassifier(n_layers=3, verbose=0, ignore_warnings=True)
start = time()
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
print(f"\n\n Elapsed: {time()-start} seconds \n")
model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)
display(models)
```
## Contributing
Your contributions are welcome, and valuable. Please, make sure to __read__ the [Code of Conduct](CONTRIBUTING.md) first. If you're not comfortable with Git/Version Control yet, please use [this form](https://forms.gle/tm7dxP1jSc75puAb9) to provide a feedback.
In Pull Requests, let's strive to use [`black`](https://black.readthedocs.io/en/stable/) for formatting files:
```bash
pip install black
black --line-length=80 file_submitted_for_pr.py
```
A few things that we could explore are:
- Enrich the [tests](#Tests)
- Any benchmarking of `nnetsauce` models can be stored in [demo](/nnetsauce/demo) (notebooks) or [examples](./examples) (flat files), with the following naming convention: `yourgithubname_yyyymmdd_shortdescriptionofdemo.[py|ipynb|R|Rmd]`
## Tests
**Ultimately**, tests for `nnetsauce`'s features **will** be located [here](nnetsauce/tests). In order to run them and obtain tests' coverage (using [`nose2`](https://nose2.readthedocs.io/en/latest/)), you'll do:
- Install packages required for testing:
```bash
pip install nose2
pip install coverage
```
- Run tests (in cloned repo) and print coverage:
```bash
make run-tests
make coverage
```
## API Documentation
- [https://techtonique.github.io/nnetsauce/](https://techtonique.github.io/nnetsauce/)
## Citation (BibTeX entry)
Replace `Version x.x.x` by the version number you've used.
```
@misc{moudiki2019nnetsauce,
author={Moudiki, T.},
title={nnetsauce, {A} package for {S}tatistical/{M}achine {L}earning using {R}andomized and {Q}uasi-{R}andomized (neural) networks.},
howpublished={\url{https://github.com/Techtonique/nnetsauce}},
note={BSD 3-Clause Clear License. Version x.x.x},
year={2019--2024}
}}
```
## References
- Deep Quasi-Randomized neural Networks for classification (2024)
https://www.researchgate.net/publication/380701207_Deep_Quasi-Randomized_neural_Networks_for_classification
- Probabilistic Forecasting with nnetsauce (using Density Estimation, Bayesian inference, Conformal prediction and Vine copulas): nnetsauce presentation at sktime meetup (2024) https://www.researchgate.net/publication/382589729_Probabilistic_Forecasting_with_nnetsauce_using_Density_Estimation_Bayesian_inference_Conformal_prediction_and_Vine_copulas
- Probabilistic Forecasting with Randomized/Quasi-Randomized networks at the International Symposium on Forecasting (2024) https://www.researchgate.net/publication/381957724_Probabilistic_Forecasting_with_RandomizedQuasi-Randomized_networks_presentation_at_the_International_Symposium_on_Forecasting_2024
- Moudiki, T. (2024). Regression-based machine learning classifiers. Available at: https://www.researchgate.net/publication/377227280_Regression-based_machine_learning_classifiers
- Moudiki, T. (2020). Quasi-randomized networks for regression and classification, with two shrinkage parameters. Available at: https://www.researchgate.net/publication/339512391_Quasi-randomized_networks_for_regression_and_classification_with_two_shrinkage_parameters
- Moudiki, T. (2019). Multinomial logistic regression using quasi-randomized networks. Available at: https://www.researchgate.net/publication/334706878_Multinomial_logistic_regression_using_quasi-randomized_networks
- Moudiki T, Planchet F, Cousin A (2018). “Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks. ”Risks, 6(1), 22. Available at: https://www.mdpi.com/2227-9091/6/1/22
## License
[BSD 3-Clause](LICENSE) © Thierry Moudiki, 2019.