ot-markov-distances 1.0.3

Differentiable distances for graphs and markov chains using optimal transport

Differentiable distances on graphs based on optimal transport

This is the implementation code for

Brugere, T., Wan, Z., & Wang, Y. (2023). Distances for Markov Chains, and Their Differentiation. ArXiv, abs/2302.08621.

Setup

Installing as a library

The ot_markov_distances package can be installed with the following command:

pip install ot-markov-distances

If for some reason you need to use cuda11.8 (ie you are installing torch+cuda118) then use the following command instead

pip install git+https://github.com/YusuLab/ot_markov_distances@cuda118

Dependencies

Python version

This project requires python 3.10 a minima. If your python version is prior to 3.10, you need to update (or to create a new conda environment) to a version above (latest release at the time of writing is 3.12)

Python dependencies

Note

The main branch uses the default (cuda12) version of torch in its dependencies. If for some reason you need to use cuda11.8, clone the cuda118 branch instead

This package manages its dependencies via poetry. I recommend you install it (otherwise if you prefer to manage them manually, a list of the dependencies is available in the file pyproject.toml)

When you have poetry, you can add dependencies using our makefile

Note

If you want to create a virtual environment for this project (as opposed to using the one you are currently in) you can use the command poetry env use python3.12 (or other python version)

$ make .make/deps

or directly with poetry

$ poetry install

TUDataset

If you are planning to reproduce the classification experiment.

The TUDataset package is also needed to run the classification experiment, but it is not available via pip / poetry. To install it, follow the instruction in the tudataset repo, including the “Compilation of kernel baselines” section, and add the directory where you downloaded it to your $PYTHONPATH. eg:

$ export PYTHONPATH="/path/to/tudataset:$PYTHONPATH"

Project structure

.
├── docs    #contains the generated docs (after typing make)
│   ├── build
│   │   └── html            #Contains the html docs in readthedocs format
│   └── source
├── experiments             #contains jupyter notebooks with the experiments
│   └── utils               #contains helper code for the experiments
├── ot_markov_distances     #contains reusable library code for computing and differentiating the discounted WL distance
│   ├── discounted_wl.py    # implementation of our discounted WL distance
│   ├── __init__.py
│   ├── sinkhorn.py         # implementation of the sinkhorn distance
│   ├── utils.py            # utility functions
│   └── wl.py               #implementation of the wl distance by Chen et al.
├── staticdocs #contains the static source for the docs
│   ├── build
│   └── source
└── tests #contains sanity checks

Documentation

The documentation is available online: read the documentation

Warning

Do not edit the documentation directly in the docs/ folder, that folder is wiped every time the documentation is built. The static parts of the documentation can be edited in staticdocs/.

You can build documentation and run tests using

$ make

Alternatively, you can build only the documentation using

$ make .make/build-docs

The documentation will be available in docs/build/html in the readthedocs format

Running Experiments

Running experiments requires installing development dependencies. This can be done by running

$ make .make/dev-deps

or alternatively

$ poetry install --with dev

Experiments can be found in the experiments/ directory (see Project structure ).

The Barycenter and Coarsening experiments can be found in experiments/Barycenter.ipynb and experiments/Coarsening.ipynb.

The performance graphs are computed in experiments/Performance.ipynb

Classification experiment

The Classification experiment (see the first paragraph of section 6 in the paper) is not in a jupyter notebook, but accessible via a command line.

As an additional dependency it needs tudataset, which is not installable via pip. To install it follow the instructions in the tudataset repo. , including the “Compilation of kernel baselines” section, and add the directory where you downloaded it to your $PYTHONPATH.

Now you can run the classification experiment using the command

$ poetry run python -m experiments.classification
usage: python -m experiments.classification [-h] {datasets_info,distances,eval} ...

Run classification experiments on graph datasets

positional arguments:
  {datasets_info,distances,eval}
    datasets_info       Print information about given datasets
    distances           Compute distance matrices for given datasets
    eval                Evaluate a kernel based on distance matrix

options:
  -h, --help            show this help message and exit

The yaml file containing dataset information that should be passed to the command line is in experiments/grakel_datasets.yaml. Modifying this file should allow running the experiment on different datasets.

FAQ

I have a question about the paper

In this case just send me an email through the email address mentioned in the paper.

I have noticed a bug in the code

Please use the Github “Issues” feature to open a ticket, and post a description of the bug, the error message and a minimal reproducible example . I’ll try to fix it.

Or if you have fixed it, you can submit a Pull Request directly

I cannot install the library

If you followed all the instructions correctly, please create a ticket using Github Issues.

Why do you need python3.10 ?

Because I am using structural pattern matching, and some typing features such as this one .