roughsets-base 1.0.1.2

RoughSets library (Pandas version)

The goal of the library is to provide base functions of Rough Sets Theory and give foundation to build extensions which will provide different methods built on RoughSets Theory, like:

• pre-processing methods
• find core and reducts
• classifiers
• post-processing methods

The library doesn't use basic loops so should help to build very fast extensions when large datasets will be used.

The library implements these main functions:

• computation of indiscernibilty relations - function: get_indiscernibility_relations

• computation of a lower and upper approximations, boundary and negative regions - all these 4 boundaries are computed by function: get_approximation_indices. For optimization, only indices of X,y are returned by the function, so can be used for futher computations
  before slicing with X and y.

The library has included unit tests for different datasets, subsets and concepts.

Requirements

Python >= 3.8
OS: Linux, Windows

Install from PyPi server

pip install roughsets-base

Build and install the library from source code

pip install --upgrade pip
pip install --upgrade build

On linux: python3 -m build

On Windows:
py -m build

pip install dist/roughsets_base--py3-none-any.whl

Install CI and dev tools

pip install -r requirements.dev.txt

Unit tests

File tests/test_dataset_KDD.py contains tests which use KDD99 dataset.
By default file will be downloaded from: http://kdd.ics.uci.edu/databases/kddcup99/corrected.gz
If You have the file on Your system You can set OS environment variable ROUGHSETS_KDD99_TEST_DATA_FOLDER
with the path to th file.
File tests/KDD99_compare_with_R_RoughSets.R contains R script which generate refernece data using well knwon reference library: RoughSets.
See: https://www.rdocumentation.org/packages/RoughSets/topics/RoughSets-package (R language). Reference datasets with results from R-RoughSets library are saved in folder tests/datasets/KDD99.
You can disable running tests for specific dataset in file test_dataset_X.py (X - symbol of dataset), method setUp.

For checking of unit tests' results was used algorithms from well known reference library:
https://www.rdocumentation.org/packages/RoughSets/topics/RoughSets-package

Re-Build sphinx documentation

pip install -r requirements.dev.txt
sphinx-build -b html ./doc ./doc/_build/html
sphinx-build -b man ./doc ./doc/_build/man

Recommended packages

sklearn-pandas https://github.com/scikit-learn-contrib/sklearn-pandas
pandas ecosystem: https://pandas.pydata.org/community/ecosystem.html

Citation

If You would like to use this library in Your research, please cite it.

Jankowski, D. (2022). Roughsets-base: Python library for base Roughsets methods. (v1.0.1) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.5957474