/partial_labelling

Learning with Partial Supervision

Primary LanguagePythonMIT LicenseMIT

Partial Labelling Approached with through Structured Prediction (PLASP)

Topic:Generic implementations of weakly supervision algorithms developped in [CAB20], [CAB21a], [CAB21b].
Author: Vivien Cabannes
Version: 1.0.0 of 2021/06/07

WARNING: For a fast implementation of (continuous) Laplacian spectral embedding, see https://github.com/VivienCabannes/laplacian

Installation

From wheel

You can download our package from its pypi repository.

$ pip install plasp

From source

You can download source code at https://github.com/VivienCabannes/partial_labelling/archive/master.zip. Once download, our packages can be install through the following command.

$ 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:

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

See Also

A standalone package for fast computation of the Laplacian decomposition can be found at: https://github.com/VivienCabannes/laplacian

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., NeurIPS, 2021