Minimal Achievable Sufficient Statistic Learning
What is this?
This code reproduces all experiments in the paper Minimal Achievable Sufficient Statistic Learning.
The code in this repo is licensed under the MIT license.
How do I run your code?
Install the Conda python package manager. Then follow the instructions here
using the file
environment.yml file in this library's root directory to satisfy the python requirements to run this library's code.
In theory our code is operating-system-agnostic, but we ran all our experiments on Ubuntu Linux, so that's where you're most likely to have installation success.
We included as many unit tests as we could. They're in the
tests directory, whose directory structure mirrors that of the rest of the library.
You can run them from the library's root directory with
python -m unittest.
Running the experiments from the paper
The scripts that run the experiments are in the
MASS-Learning conda environment, adjust the options in the scripts to suit your machine, and run whatever experiments you like from the library's root directory as a module, e.g.
python -m scripts.paper_tables.SmallMLPAccRegUQOOD.
Experiments log their results in the
runs directory. You can activate tensorboard to watch their progress.
Once the experiments are done, the scripts in
scripts/plotting will consume the logs and give you the results from the paper.
Feel free to get in touch with Milan Cvitkovic or any of the other paper authors. We'd love to hear from you!