https://github.com/omadson/scikit-mlm
Minimal Learning Machine implementation using the scikit-learn API.
https://github.com/omadson/scikit-mlm
classification hacktoberfest machine-learning machine-learning-algorithms machine-learning-api machine-learning-library machine-learning-models minimal-learning-machine mlm pypi pypi-package python python3 regression scikit-learn
Last synced: about 1 month ago
JSON representation
Minimal Learning Machine implementation using the scikit-learn API.
- Host: GitHub
- URL: https://github.com/omadson/scikit-mlm
- Owner: omadson
- License: mit
- Created: 2018-09-25T19:03:58.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-02-11T00:36:10.000Z (over 2 years ago)
- Last Synced: 2025-04-04T17:50:14.951Z (about 2 months ago)
- Topics: classification, hacktoberfest, machine-learning, machine-learning-algorithms, machine-learning-api, machine-learning-library, machine-learning-models, minimal-learning-machine, mlm, pypi, pypi-package, python, python3, regression, scikit-learn
- Language: Python
- Homepage: https://pypi.org/project/scikit-mlm/
- Size: 700 KB
- Stars: 10
- Watchers: 2
- Forks: 2
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# scikit-mlm
[](http://pypi.org/project/scikit-mlm/)
[](https://github.com/omadson/scikit-mlm/pulse)
[](https://github.com/omadson/scikit-mlm/commit/master)
[](https://pepy.tech/project/scikit-mlm)
[](https://doi.org/10.5281/zenodo.2875802)
`scikit-mlm` is a Python module implementing the [Minimal Learning Machine][1] (MLM) machine learning technique using the [scikit-learn][2] API.
## instalation
the `scikit-mlm` package is available in [PyPI](https://pypi.org/project/scikit-mlm/). to install, simply type the following command:
```
pip install scikit-mlm
```
### Having problems?
- you may need to use the `--user` flag for the commands above to install in a non-system location (depends on your environment). alternatively, you can execute the `pip` commands with `sudo` (not recommended).
- you may need to add the `--use-wheel` option if you have an older `pip` version (wheels are now the default binary package format for `pip`).
## basic usage
example of classification with the [nearest neighbor MLM](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec9) classifier:
```Python
from skmlm import NN_MLM
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.datasets import load_iris
# load dataset
dataset = load_iris()
clf = make_pipeline(MinMaxScaler(), NN_MLM(rp_number=20))
scores = cross_val_score(clf, dataset.data, dataset.target, cv=10, scoring='accuracy')
print('AVG = %.3f, STD = %.3f' % (scores.mean(), scores.std()))
```
## how to cite scikit-mlm
if you use `scikit-mlm` in your paper, please cite it in your publication.
```
@misc{scikit-mlm,
author = "Madson Luiz Dantas Dias",
year = "2019",
title = "scikit-mlm: An implementation of {MLM} for scikit-learn framework",
url = "https://github.com/omadson/scikit-mlm",
doi = "10.5281/zenodo.2875802",
institution = "Federal University of Cear\'{a}, Department of Computer Science"
}
```
## contributing
this project is open for contributions. here are some of the ways for you to contribute:
- bug reports/fix
- features requests
- use-case demonstrations
to make a contribution, just fork this repository, push the changes in your fork, open up an issue, and make a pull request!
## list of implemented technics
- [original regression (MLMR)](https://doi.org/10.1016/j.neucom.2014.11.073)
- [original classification (MLMC)](https://doi.org/10.1016/j.neucom.2014.11.073)
- [nearest neighbor MLM (NN_MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec9)
- [opposite neighborhood MLM (ON_MLM)](https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-198.pdf)
- [fuzzy C-means MLM (FCM_MLM)](https://doi.org/10.1007/978-3-319-95312-0_34)
- [optimally selected MLM (OS_MLM)](https://doi.org/10.1007/978-3-030-03493-1_70)
- [ℓ1/2-norm regularization MLM (L12_MLM)](https://doi.org/10.1109/BRACIS.2018.00043)
- [weighted MLM (w_MLM)](https://doi.org/10.1007/978-3-319-26532-2_61)
- [ranking MLM (R_MLM)](https://doi.org/10.1109/BRACIS.2015.39) (WIP)
- [cubic equation MLM (C_MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec10)
## future improvements
list of methods that will be implemented in the next releases:
- [expected squared distance MLM (ESD-MLM)](https://doi.org/10.1007/978-3-319-26532-2_62)
- [voting based MLM (V-MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec11)
- [weighted voting based MLM (WV-MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec11)
- [random sampling voting based MLM (RSV-MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec11)
- [random sampling weighted voting based MLM (RSWV-MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec11)
- [reject option MLM (renjo-MLM)](https://doi.org/10.1109/BRACIS.2016.078)
- [reject option weighted MLM (renjo-wMLM)](https://doi.org/10.1109/BRACIS.2016.078)
## contributors
- [Madson Dias](https://github.com/omadson)
## acknowledgement
- thanks for [@JamesRitchie](https://github.com/JamesRitchie), the initial idea of this project is inspired on the [scikit-rvm](https://github.com/JamesRitchie/scikit-rvm) repo
[1]: https://doi.org/10.1016/j.neucom.2014.11.073
[2]: http://scikit-learn.org/
[3]: https://doi.org/10.1007/s11063-017-9587-5#