Guarantees-Based Mechanistic Interpretability

Setup

The code can be run under any environment with Python 3.9 and above.

We use poetry for dependency management, which can be installed following the instructions here.

To build a virtual environment with the required packages, simply run

poetry config virtualenvs.in-project true
poetry install

Notes

• On some systems you may need to set the environment variable PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring to avoid keyring-based errors.
• The first line tells poetry to create the virtual environment in the project directory, which allows VS Code to find the virtual environment.
• If you are using caches from other machines, if you see errors like "dbm.error: db type is dbm.gnu, but the module is not available", you can probably solve the issue by following instructions from StackOverflow:
  • sudo apt-get install libgdbm-dev python3-gdbm
  • If you are using conda or some other Python version management, you can inspect the output of dpkg -L python3-gdbm and copy the lib-dynload/_gdbm.cpython-*-x86_64-linux-gnu.so file to the corresponding lib/ directory associated to the python you are using.

Running notebooks

To open a Jupyter notebook, run

poetry run jupyter lab

If this doesn't work (e.g. you have multiple Jupyter kernels already installed on your system), you may need to make a new kernel for this project:

poetry run python -m ipykernel install --user --name=gbmi

Reproducing the plots and numbers in the figures

You can run

poetry run python notebooks/max_of_4_all_models.py

to produce the data, and the plots can be seen with

poetry run python notebooks/max_of_4.py

There are also corresponding .ipynb files for each of these.

See also .github/workflows/ci.yml.

You can delete notebooks/.cache to regenerate all the proofs and analyses from scratch rather than loading them from cache.