oslili 0.15

Open Source License Identification Library

OSLiLi - Open Source License Identification Library

Open Source License Identification Library is an experimental code, that use Scikit-learn to implement a Multinomial Naive Bayes classifier trained with SPDX data to identify Open Source Licenses. This should be consider as a proof of concept for identify Open Source licenses using Machine Learning.

This is an experimental project, please don't use it for production. For a more robust implementation, please check the project Askalono https://github.com/jpeddicord/askalono

Usage

On the command line

You can use OSLiLi in your terminal as command line, please install the oslili-cli package:

$ pip3 install oslili-cli
$ oslili-cli LICENSE
License: MIT (0.89 probability)
Copyright: ('2021', '(c)  Andrew Barrier')

As a library

In order to use the library, you need to import and use identify_license or identify_copyright.

import argparse
from oslili import LicenseAndCopyrightIdentifier


def main():
    msg = 'Identify open source license and copyright statements'
    parser = argparse.ArgumentParser(description=msg)
    parser.add_argument('file_path', help='Path to the file to analyze')
    args = parser.parse_args()
    file_path = args.file_path

    with open(args.file_path, 'r') as f:
        text = f.read()

    identifier = LicenseAndCopyrightIdentifier()
    license_spdx_code, license_proba = identifier.identify_license(text)
    print(f'License: {license_spdx_code} ({license_proba:.2f} probability)')
    year_range, statement = identifier.identify_copyright(text)
    if statement:
        if None not in statement:
            print(f'Copyright: {statement}')


if __name__ == '__main__':
    main()

Notice

This tool does not provide legal advice; I'm not a lawyer.

The code is an experimental implementation to match your input to a database of similar license texts and tell you if it's a close match. Refrain from relying on the accuracy of the output of this tool.

Remember: The tool can't tell you if a license works for your project or use case. Please should seek independent legal advice for any licensing questions.

Where do the licenses come from?

License SPDX dataset is sourced directly from SPDX: https://github.com/spdx/license-list-data.

Datasets for ML training were generated scanning different sources, and inspired by two academic publications:

• Machine Learning-Based Detection of Open Source License Exceptions: C. Vendome, M. Linares-Vásquez, G. Bavota, M. Di Penta, D. German and D. Poshyvanyk, "Machine Learning-Based Detection of Open Source License Exceptions," 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE), Buenos Aires, 2017, pp. 118-129, doi: 10.1109/ICSE.2017.19.

• A Machine Learning Method for Automatic Copyright Notice Identification of Source Files: Shi QIU, German M. DANIEL, Katsuro INOUE, A Machine Learning Method for Automatic Copyright Notice Identification of Source Files, IEICE Transactions on Information and Systems, 2020, Volume E103.D, Issue 12, Pages 2709-2712, Released December 01, 2020, Online ISSN 1745-1361, Print ISSN 0916-853.

Contributing

Contributions are very welcome! See CONTRIBUTING for more info.

License

This library is licensed under the Apache 2.0 License.