Calculate Jaccard metrics for boolean values.
The source code lives on github.
The documentation lives at ReadTheDocs.
The project can be installed from PyPI.
The code here represents a python implementation of the Jaccard package hosted here by N. Chung. Its citation follows.
Chung, N., Miasojedow, B., Startek, M., and Gambin, A. "Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data" BMC Bioinformatics (2019) 20(Suppl 15): 644. https://doi.org/10.1186/s12859-019-3118-5
It's a PyPI package, so the pocess is pretty straightforward:
pip install -U boolean_jaccard # for most recent version
pip install -U boolean_jaccard==0.0.1 # for a specific versionA list of all released versions can be found at our tags.
boolean_jaccard uses strict automated semantic versioning.
As such,
we guarantee bugfixes in path releases,
backwards compatible features in minor releases,
and breaking changes in major releases.
We will endeavour to avoid breaking changes where possible,
but,
should they occur,
they will only be in major releases.
Most users **will not need** these instructions.
If you need to customise the code in some manner, you'll need to install from source. To do that, either clone the repository from github, or download one of our releases. For full instructions, please see our guide on contributing.
Open-source software is only open-source becaues of the excellent community, so we welcome any and all contributions! If you think you have found a bug, please log a report in our issues. If you think you can fix a bug, or have an idea for a new feature, please see our guide on contributing for more information on how to get started! While here, we request that you follow our code of conduct to help maintain a welcoming, respectful environment.
- Fully vectorise to improve performance.
If you use boolean_jaccard in your work,
please cite the following manuscripts:
- Chung, N., Miasojedow, B., Startek, M., and Gambin, A. "Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data" BMC Bioinformatics (2019) 20(Suppl 15): 644. https://doi.org/10.1186/s12859-019-3118-5