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

$ make .make/deps

$ poetry install

$ 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

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.

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 .