Skip to main content

Learning with Partial Supervision

Project description


Generic implementations of weakly supervision algorithms developped in [CAB20], [CAB21a], [CAB21b].


Vivien Cabannes

1.0.0 of 2021/06/07


To install our package, run the setup file

$ python <path to code folder>/ install

You can also install it in develop mode, eventually with pip

$ cd <path to code folder>
$ pip install -e .


See files:
  • problems/classification/

  • problems/classification/

  • and more generally *

Package Requirements

Most of the code is based on the following python libraries:
  • numpy

  • numba

  • matplotlib

Some testing done with notebook are based on:
  • jupyter-notebook

  • ipywidgets

For ranking, we used the following lp solver library:
  • cplex

To load LIBSVM files, more precisely to read libsvm files format we used:
  • scikit-learn

To load MULAN files, more precisely to read mulan files format we used:
  • arff

  • skmultilearn



Structured Prediction with Partial Labelling through the Infimum Loss, Cabannes et al., ICML, 2020


Disambiguation of weak supervision with exponential convergence rates, Cabannes et al., ICML, 2021


Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning, Cabannes et al., Preprint, 2021

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

plasp-1.0.0.tar.gz (28.8 kB view hashes)

Uploaded source

Built Distribution

plasp-1.0.0-py3-none-any.whl (45.9 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page