Miscellaneous Statistical/Machine learning stuff.
Installation for Python and R |
Package description |
Quick start |
Contributing |
Tests |
Dependencies |
Citing mlsauce
|
API Documentation |
References |
License
- 1st (preferred) method: from Github, for the development version
pip install git+https://github.com/Techtonique/mlsauce.git --verbose
- 2nd method: using
conda
conda install -c conda-forge mlsauce
(Note to self or developers: https://github.com/conda-forge/mlsauce-feedstock and https://conda-forge.org/docs/maintainer/adding_pkgs.html#step-by-step-instructions)
Only for Linux, for now. Windows users can envisage using WSL, the Windows Subsystem for Linux.
From GitHub
remotes::install_github("Techtonique/mlsauce_r") # the repo is in this organization
From R-universe
install.packages('mlsauce', 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):
?mlsauce::AdaOpt
For a list of available models, visit https://techtonique.github.io/mlsauce/.
Miscellaneous Statistical/Machine learning stuff. See next section.
Examples can be found here on GitHub. You can also read about this package here, and in particular for LSBoost
: https://thierrymoudiki.github.io/blog/#LSBoost.
Your contributions are welcome, and valuable. Please, make sure to read the Code of Conduct first. If you're not comfortable with Git/Version Control yet, please use this form to provide a feedback.
In Pull Requests, let's strive to use black
for formatting files:
pip install black
black --line-length=80 file_submitted_for_pr.py
A few things that we could explore are:
- Enrich the tests
- Continue to make
mlsauce
available toR
users --> here - Any benchmarking of
mlsauce
models can be stored in demo (notebooks) or examples (flat files), with the following naming convention:yourgithubname_ddmmyy_shortdescriptionofdemo.[py|ipynb|R|Rmd]
Ultimately, tests for mlsauce
's features will be located here. In order to run them and obtain tests' coverage (using nose2
), you'll do:
- Install packages required for testing:
pip install nose2
pip install coverage
- Run tests and print coverage:
git clone https://github.com/thierrymoudiki/mlsauce.git
cd mlsauce
nose2 --with-coverage
- Obtain coverage reports:
At the command line:
coverage report -m
or an html report:
coverage html
Note to self and developpers: https://conda-forge.org/docs/maintainer/adding_pkgs.html#step-by-step-instructions
- Numpy
- Scipy
- scikit-learn
- querier
@misc{moudiki2019mlsauce,
author={Moudiki, Thierry},
title={\code{mlsauce}, {M}iscellaneous {S}tatistical/{M}achine {L}earning stuff},
howpublished={\url{https://github.com/thierrymoudiki/mlsauce}},
note={BSD 3-Clause Clear License. Version 0.x.x.},
year={2019--2020}
}
-
Moudiki, T. (2020). LSBoost, gradient boosted penalized nonlinear least squares. Available at: https://www.researchgate.net/publication/346059361_LSBoost_gradient_boosted_penalized_nonlinear_least_squares
-
Moudiki, T. (2020). AdaOpt: Multivariable optimization for classification. Available at: https://www.researchgate.net/publication/341409169_AdaOpt_Multivariable_optimization_for_classification
BSD 3-Clause © Thierry Moudiki, 2019.
This package was created with Cookiecutter and the project template.