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‎.github/workflows/python-publish.yml

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# This workflow will upload a Python Package using Twine when a release is created
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# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
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# This workflow uses actions that are not certified by GitHub.
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# They are provided by a third-party and are governed by
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# separate terms of service, privacy policy, and support
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# documentation.
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name: Upload Python Package
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on:
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release:
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types: [published]
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jobs:
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deploy:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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- name: Set up Python
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uses: actions/setup-python@v2
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with:
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python-version: '3.x'
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install build
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- name: Build package
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run: python -m build
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- name: Publish package
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uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
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with:
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user: __token__
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password: ${{ secrets.PYPI_API_TOKEN }}

‎.github/workflows/python-pytest.yml

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# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
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# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
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name: Python package
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on:
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push:
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branches: [ master ]
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pull_request:
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branches: [ master ]
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jobs:
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build:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: ["3.8", "3.9", "3.10"]
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steps:
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- uses: actions/checkout@v2
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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python -m pip install pytest
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if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
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- name: Test with pytest
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run: |
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pytest

‎CITATIONS.bib

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@article{bjorklund2022robust,
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title = {Robust regression via error tolerance},
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author = {Bj{\"o}rklund, Anton and Henelius, Andreas and Oikarinen, Emilia and Kallonen, Kimmo and Puolam{\"a}ki, Kai},
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year = {2022},
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month = jan,
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journal = {Data Mining and Knowledge Discovery},
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issn = {1384-5810, 1573-756X},
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doi = {10.1007/s10618-022-00819-2}
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}
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@inproceedings{bjorklund2019sparse,
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title = {Sparse Robust Regression for Explaining Classifiers},
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booktitle = {Discovery Science},
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author = {Bj{\"o}rklund, Anton and Henelius, Andreas and Oikarinen, Emilia and Kallonen, Kimmo and Puolam{\"a}ki, Kai},
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year = {2019},
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series = {Lecture Notes in Computer Science},
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volume = {11828},
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pages = {351--366},
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publisher = {Springer International Publishing},
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doi = {10.1007/978-3-030-33778-0_27},
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isbn = {978-3-030-33777-3 978-3-030-33778-0}
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}

‎LICENSE

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MIT License
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Copyright (c) 2019 Aggrathon
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Copyright (c) 2022 Anton Björklund
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

‎README.md

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![Example of Robust Regression](examples/pyslise_banner.png)
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![PySLISE Banner Image](examples/pyslise_banner.png)
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# SLISE - Sparse Linear Subset Explanations
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Python implementation of the SLISE algorithm. The SLISE algorithm can be used for
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both robust regression and to explain outcomes from black box models.
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For more details see [the paper](https://rdcu.be/bVbda), alternatively for a more informal
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overview see [the presentation](https://github.com/edahelsinki/slise/raw/master/vignettes/presentation.pdf),
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or [the poster](https://github.com/edahelsinki/slise/raw/master/vignettes/poster.pdf).
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Python implementation of the SLISE algorithm. The SLISE algorithm can be used for both robust regression and to explain outcomes from black box models.
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For more details see [the original paper](https://rdcu.be/bVbda) or the [robust regression paper](https://rdcu.be/cFRHD).
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Alternatively for a more informal overview see [the presentation](https://github.com/edahelsinki/slise/raw/master/vignettes/presentation.pdf), or [the poster](https://github.com/edahelsinki/slise/raw/master/vignettes/poster.pdf).
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> **Björklund A., Henelius A., Oikarinen E., Kallonen K., Puolamäki K.**
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> *Sparse Robust Regression for Explaining Classifiers.*
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> *Björklund A., Henelius A., Oikarinen E., Kallonen K., Puolamäki K.* (2019)
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> **Sparse Robust Regression for Explaining Classifiers.**
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> Discovery Science (DS 2019).
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> Lecture Notes in Computer Science, vol 11828, Springer.
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> https://doi.org/10.1007/978-3-030-33778-0_27
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> https://doi.org/10.1007/978-3-030-33778-0_27
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> *Björklund A., Henelius A., Oikarinen E., Kallonen K., Puolamäki K.* (2022).
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> **Robust regression via error tolerance.**
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> Data Mining and Knowledge Discovery.
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> https://doi.org/10.1007/s10618-022-00819-2
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## The idea
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> SLISE is a robust regression algorithm, which means that it is able to handle outliers. This is in contrast to, e.g., ordinary least-squares regression, which gives skewed results when outliers are present.
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> ![Example of Robust Regression](examples/ex2.png)
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> ![Example of Explanation](examples/ex2.png)
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> SLISE can also be used to explain outcomes from black box models by locally approximating the complex models with a simpler linear model.
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## Dependencies
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This implementation is requires Python 3 and the following packages:
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This implementation requires Python 3 and the following packages:
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- matplotlib
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- numba

‎requirements.txt

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numpy>=1.19
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scipy>=1.4
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PyLBFGS>=0.2
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numba=>0.53
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numba>=0.53
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matplotlib>=3.3

‎setup.cfg

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[metadata]
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name = slise
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version = 1.1.2
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version = 2.0.0
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author = Anton Björklund
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author_email = [email protected]
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description = The SLISE algorithm for robust regression and explanations of black box models
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Robust regression
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Sparse Linear Regression
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Explainable Artificial Intelligence
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Local Explanations
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[options]
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packages = find:

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