Topic:
Generic implementations of weakly supervision algorithms
developped in [CAB20] , [CAB21a] , [CAB21b] .
Author:
Vivien Cabannes
Version:
1.0.0 of 2021/06/07
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 .
See files:
problems/classification/libsvm_experiments.pyproblems/classification/semi_supervision_experiments.pyand more generally *_experiements.py
Most of the code is based on the following python libraries:
Some testing done with notebook are based on:
jupyter-notebook
ipywidgets
For ranking, we used the following lp solver library:
To load LIBSVM files, more precisely to read libsvm files format we used:
To load MULAN files, more precisely to read mulan files format we used:
Datasets can be download at:
Change path in config file dataloader/config.py to specify path to your data.
[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
