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scikit-mlm is a Python module implementing theMinimal Learning Machine (MLM) machine learning technique using thescikit-learn API.

thescikit-mlm package is available inPyPI. to install, simply type the following command:

pip install scikit-mlm

• 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 thepip commands withsudo (not recommended).
• you may need to add the--use-wheel option if you have an olderpip version (wheels are now the default binary package format forpip).

example of classification with thenearest neighbor MLM classifier:

fromskmlmimportNN_MLMfromsklearn.preprocessingimportMinMaxScalerfromsklearn.model_selectionimportcross_val_scorefromsklearn.pipelineimportmake_pipelinefromsklearn.datasetsimportload_iris# load datasetdataset=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()))

if you usescikit-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" }

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!

• original regression (MLMR)
• original classification (MLMC)
• nearest neighbor MLM (NN_MLM)
• opposite neighborhood MLM (ON_MLM)
• fuzzy C-means MLM (FCM_MLM)
• optimally selected MLM (OS_MLM)
• ℓ_1/2-norm regularization MLM (L12_MLM)
• weighted MLM (w_MLM)
• ranking MLM (R_MLM) (WIP)
• cubic equation MLM (C_MLM)

list of methods that will be implemented in the next releases:

• expected squared distance MLM (ESD-MLM)
• voting based MLM (V-MLM)
• weighted voting based MLM (WV-MLM)
• random sampling voting based MLM (RSV-MLM)
• random sampling weighted voting based MLM (RSWV-MLM)
• reject option MLM (renjo-MLM)
• reject option weighted MLM (renjo-wMLM)

• Madson Dias

• thanks for@JamesRitchie, the initial idea of this project is inspired on thescikit-rvm repo