scikit-weak (scikit-weakly-supervised)
A package featuring utilities and algorithms for weakly supervised ML. Should be (more-or-less) compatible with scikit-learn! It collects original algorithms and methods developed by the contributors, as well as some algorithms available in the literature.
Current contributors:
- Andrea Campagner, MUDI Lab, University of Milano-Bicocca
- Julian Lienen, Paderborn University
How to install
You can install the library using the command:
pip install scikit-weak
Dependencies:
numpy, scipy, scikit-learn, tensorflow, keras, pytest
Documentation
The documentation is generated using Sphinx (https://www.sphinx-doc.org/). If you download the source code from this repository you can generate the documentation in html format by typing:
pip install sphinx-rtd-theme
sphinx-build -b html docs/source docs/build/html
in the main folder of the project.
1 maintainer
Metadata
1 maintainer
Release files for scikit-weak 0.2.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| scikit-weak-0.2.1.tar.gz | 21.4 kB | Details |
Release files / scikit-weak-0.2.1.tar.gz
| Download URL | scikit-weak-0.2.1.tar.gz |
|---|---|
| Size | 21.4 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
1e05c36aab63040f3951a6c159b344cdc02cab92b5636d97ef59bbafe966f4c0
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496c284478de71eb54c46e9ac263a5835abaaae852a40eb38bf4d99989e6dd47
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twine/4.0.0 CPython/3.10.4
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