Partial Gromov-Wasserstein with applications on Positive-Unlabeled Learning

This code is an attempt to help reproduce results from the paper "Partial Gromov-Wasserstein with applications on Positive-Unlabeled Learning" https://arxiv.org/abs/2002.08276.

Pre-requisites

This code is Python3 code and relies on the following libraries:

pot
math
numpy
pandas
matplotlib
sklearn
torch
torchvision
scipy
numba
geoopt
torch
mpl_toolkits
time
IPython

Dependencies can be installed via:

pip install -r requirements.txt

(maybe the torch dependency will raise an error and an alternative install method will be prompted).

Available scripts

For figures 1 to 3, notebook that generate the figures is provided.

For tables 1 & 2, notebook that generate the performances relative to the partial-W and partial-GW results is provided. The results regarding competitors have been obtained using the following github repository https://github.com/MasaKat0/PUlearning from the paper

Masahiro Kato, Takeshi Teshima, Junya Honda 
Learning from Positive and Unlabeled Data with a Selection Bias. 
In ICLR, 2019.

To run notebook tab12.ipynb, first make the data folder with the following datasets:

• mushrooms, shuttle.scale, shuttle.scale.t, usps, usps.t, connect-4 from https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/
• page-blocks.data, spambase.data from https://archive.ics.uci.edu/ml/index.php
• Caltech10_zscore_SURF_L10.mat, amazon_zscore_SURF_L10.mat, webcam_zscore_SURF_L10.mat , dslr_zscore_SURF_L10.mat, caltech_decaf.mat, amazon_decaf.mat,webcam_decaf.mat, dslr_decaf.mat from ``https://github.com/jindongwang/transferlearning/blob/master/data/dataset.md#office+caltech`

(the MNIST dataset is automatically downloaded with torchvision).

partial_gw.py

This is the script that contains the main function for computing partial-GW.

Data come from the github repository https://github.com/una-dinosauria/3d-pose-baseline from the paper

Julieta Martinez, Rayat Hossain, Javier Romero, James J. Little. 
A simple yet effective baseline for 3d human pose estimation. 
In ICCV, 2017.

utils.py

This file contains functions to load the datasets, draw the different positive and unlabeled datasets and draw the figures.