{"id":32175930,"url":"https://github.com/techtonique/nnetsauce","last_synced_at":"2025-10-21T19:51:59.118Z","repository":{"id":42048407,"uuid":"287445871","full_name":"Techtonique/nnetsauce","owner":"Techtonique","description":"Statistical/Machine Learning using Randomized and Quasi-Randomized (neural) networks (currently Python \u0026 R)","archived":false,"fork":false,"pushed_at":"2025-10-08T06:35:21.000Z","size":33802,"stargazers_count":20,"open_issues_count":0,"forks_count":4,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-10-21T19:51:38.722Z","etag":null,"topics":["deep-learning","machine-learning","neural-networks","quasi-randomized","statistical-learning"],"latest_commit_sha":null,"homepage":"https://techtonique.github.io/nnetsauce/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause-clear","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Techtonique.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGES.md","contributing":"CONTRIBUTING.md","funding":".github/FUNDING.yml","license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null},"funding":{"github":"Techtonique"}},"created_at":"2020-08-14T04:48:27.000Z","updated_at":"2025-10-08T06:35:24.000Z","dependencies_parsed_at":"2023-12-07T09:23:50.594Z","dependency_job_id":"331be99b-9959-4210-a2da-ebc464bb413e","html_url":"https://github.com/Techtonique/nnetsauce","commit_stats":{"total_commits":714,"total_committers":2,"mean_commits":357.0,"dds":0.009803921568627416,"last_synced_commit":"41a9afaf8c8e6072fc9b8a8428872b774cf7e4b4"},"previous_names":[],"tags_count":57,"template":false,"template_full_name":null,"purl":"pkg:github/Techtonique/nnetsauce","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Techtonique%2Fnnetsauce","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Techtonique%2Fnnetsauce/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Techtonique%2Fnnetsauce/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Techtonique%2Fnnetsauce/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Techtonique","download_url":"https://codeload.github.com/Techtonique/nnetsauce/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Techtonique%2Fnnetsauce/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":280325298,"owners_count":26311419,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-21T02:00:06.614Z","response_time":58,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","machine-learning","neural-networks","quasi-randomized","statistical-learning"],"created_at":"2025-10-21T19:51:55.432Z","updated_at":"2025-10-21T19:51:59.109Z","avatar_url":"https://github.com/Techtonique.png","language":"Jupyter Notebook","funding_links":["https://github.com/sponsors/Techtonique"],"categories":[],"sub_categories":[],"readme":"nnetsauce\n--------\n\n![nnetsauce logo](the-nnetsauce.png)\n\n\u003chr\u003e\n\nRandomized and Quasi-Randomized (neural) networks. \n\n![PyPI](https://img.shields.io/pypi/v/nnetsauce) [![PyPI - License](https://img.shields.io/pypi/l/nnetsauce)](https://github.com/thierrymoudiki/nnetsauce/blob/master/LICENSE) [![Downloads](https://pepy.tech/badge/nnetsauce)](https://pepy.tech/project/nnetsauce) \n[![Downloads](https://anaconda.org/conda-forge/nnetsauce/badges/downloads.svg)](https://anaconda.org/conda-forge/nnetsauce)\n[![HitCount](https://hits.dwyl.com/Techtonique/nnetsauce.svg?style=flat-square)](http://hits.dwyl.com/Techtonique/nnetsauce)\n[![CodeFactor](https://www.codefactor.io/repository/github/techtonique/nnetsauce/badge)](https://www.codefactor.io/repository/github/techtonique/nnetsauce)\n[![Documentation](https://img.shields.io/badge/documentation-is_here-green)](https://techtonique.github.io/nnetsauce/)\n\n\n## Contents \n [Installing for Python and R](#installing-for-Python-and-R) |\n [Package description](#package-description) |\n [Quick start](#quick-start) |\n [Contributing](#Contributing) |\n [Tests](#Tests) |\n [Dependencies](#dependencies) |\n [Citing `nnetsauce`](#Citation) |\n [API Documentation](#api-documentation) |\n [References](#References) |\n [License](#License) \n\n\n## Installing (for Python and R)\n\n### Python \n\n- __1st method__: by using `pip` at the command line for the stable version\n\n```bash\npip install nnetsauce\n```\n\n- __2nd method__: using `conda` (Linux and macOS only for now)\n\n```bash\nconda install -c conda-forge nnetsauce \n```\n\n(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)\n\n- __3rd method__: from Github, for the development version\n\n```bash\npip install git+https://github.com/Techtonique/nnetsauce.git\n```\n\nor \n\n```bash\ngit clone https://github.com/Techtonique/nnetsauce.git\ncd nnetsauce\nmake install\n```\n\n\n### R \n\n**From GitHub**\n\n```bash\nremotes::install_github(\"Techtonique/nnetsauce_r\") # the repo is in this organization\n```\n\n**From R-universe**\n\n```bash\ninstall.packages('nnetsauce', repos = c('https://techtonique.r-universe.dev',\n'https://cloud.r-project.org'))\n```\n\n__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):\n\n```R\n?nnetsauce::MultitaskClassifier\n```\n\nFor a list of available models, visit [https://techtonique.github.io/nnetsauce/](https://techtonique.github.io/nnetsauce/).\n\n\n\n## Package description\n\nA package for Statistical/Machine Learning using Randomized and Quasi-Randomized (neural) networks. See next section. \n\n## Quick start\n\nThere 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). \n\nYou can also read these [blog posts](https://thierrymoudiki.github.io/blog/#QuasiRandomizedNN).\n\n_Lazy Deep (quasi-randomized neural) networks example_\n\n```python\n!pip install nnetsauce --upgrade\n```\n\n```python\nimport os\nimport nnetsauce as ns\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.model_selection import train_test_split\nfrom time import time\n\ndata = load_breast_cancer()\nX = data.data\ny= data.target\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123)\n\nclf = ns.LazyDeepClassifier(n_layers=3, verbose=0, ignore_warnings=True)\nstart = time()\nmodels, predictions = clf.fit(X_train, X_test, y_train, y_test)\nprint(f\"\\n\\n Elapsed: {time()-start} seconds \\n\")\n\nmodel_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)\n\ndisplay(models)\n```\n\n## Contributing\n\nYour 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.\n\nIn Pull Requests, let's strive to use [`black`](https://black.readthedocs.io/en/stable/) for formatting files: \n\n```bash\npip install black\nblack --line-length=80 file_submitted_for_pr.py\n```\n\nA few things that we could explore are:\n\n- Enrich the [tests](#Tests)\n- 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]`\n\n\n## Tests\n\n**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: \n\n- Install packages required for testing: \n\n```bash\npip install nose2\npip install coverage\n```\n\n- Run tests (in cloned repo) and print coverage:\n\n```bash\nmake run-tests\nmake coverage\n```\n\n## API Documentation\n\n- [https://techtonique.github.io/nnetsauce/](https://techtonique.github.io/nnetsauce/)\n\n\n## Citation (BibTeX entry)\n\nReplace `Version x.x.x` by the version number you've used. \n\n```\n@misc{moudiki2019nnetsauce,\nauthor={Moudiki, T.},\ntitle={nnetsauce, {A} package for {S}tatistical/{M}achine {L}earning using {R}andomized and {Q}uasi-{R}andomized (neural) networks.},\nhowpublished={\\url{https://github.com/Techtonique/nnetsauce}},\nnote={BSD 3-Clause Clear License. Version x.x.x},\nyear={2019--2024}\n}}\n```\n\n## References\n\n- Deep Quasi-Randomized neural Networks for classification (2024)\n  https://www.researchgate.net/publication/380701207_Deep_Quasi-Randomized_neural_Networks_for_classification\n\n- 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\n  \n- 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\n\n- Moudiki, T. (2024). Regression-based machine learning classifiers. Available at: https://www.researchgate.net/publication/377227280_Regression-based_machine_learning_classifiers\n\n- 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\n\n- 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\n\n- 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\n\n\n## License\n\n[BSD 3-Clause](LICENSE) © Thierry Moudiki, 2019. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftechtonique%2Fnnetsauce","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftechtonique%2Fnnetsauce","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftechtonique%2Fnnetsauce/lists"}