plasp 1.0.0

Learning with Partial Supervision

Topic:

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

Author:

Vivien Cabannes

Version:

1.0.0 of 2021/06/07

Installation

To install our package, run the setup file

$ python <path to code folder>/setup.py install

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

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

Usage

See files:

• problems/classification/libsvm_experiments.py

• problems/classification/semi_supervision_experiments.py

• and more generally *_experiements.py

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

Datasets links

Datasets can be download at:

• LIBSVM: https://www.csie.ntu.edu.tw/~cjlin/libsvm/oldfiles/index-1.0.html

• MULAN: http://mulan.sourceforge.net/datasets-mlc.html

Change path in config file dataloader/config.py to specify path to your data.

References

[CAB20]

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

[CAB21a]

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

[CAB21b]

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