falcon-challenge 0.3.3

No project description provided

FALCON Benchmark and Challenge

This package contains core code for submitting decoders to the FALCON challenge. Full github contains additional examples and documentation.

Installation

Install falcon_challenge with:

pip install falcon-challenge

To create Docker containers for submission, you must have Docker installed. See, e.g. https://docs.docker.com/desktop/install/linux-install/.

Getting started

Data downloading

The FALCON datasets are available on DANDI (or through private correspondence, if beta-testing).

NOTE FOR BETA TESTERS:

• Some of the sample code expects your data directory to be set up in ./data. Specifically, the following hierarchy is expected:

data

• h1
  • held_in_calib
  • held_out_calib
  • minival
  • eval (Note this is private data)
• m1
  • sub-MonkeyL-held-in-calib
  • sub-MonkeyL-held-out-calib
  • minival (Copy dandiset minival folder into this folder)
  • eval (Copy the ground truth held in and held out data into this folder)

H1 should unfold correctly just from unzipping the provided directory. M1 should work by renaming the provided dandiset to m1 and minival folder inside, and then copying the provided eval data into this folder. Each of the lowest level dirs holds the NWB files.

Code

This codebase contains starter code for implementing your own method for the FALCON challenge.

• The falcon_challenge folder contains the logic for the evaluator. Submitted solutions must conform to the interface specified in falcon_challenge.interface.
• In data_demos, we provide notebooks that survey each dataset released as part of this challenge.
• In decoder_demos, we provide sample decoders and baselines that are formatted to be ready for submission to the challenge. To use them, see the comments in the header of each file ending in _sample.py. Your solutions should look similar once implemented!

For example, you can prepare and evaluate a linear decoder by running:

python decoder_demos/sklearn_decoder.py --training_dir data/h1/held_in_calib/ --calibration_dir data/h1/held_out_calib/ --mode all --task h1
python decoder_demos/sklearn_sample.py --evaluation local --phase minival --split h1

Docker Submission

To interface with our challenge, your code will need to be packaged in a Docker container that is submitted to EvalAI. Try this process by building and running the provided sklearn_sample.Dockerfile, to confirm your setup works. Do this with the following commands (once Docker is installed)

# Build
docker build -t sk_smoke -f ./decoder_demos/sklearn_sample.Dockerfile .
bash test_docker_local.sh --docker-name sk_smoke

EvalAI Submission

Please ensure that your submission runs locally before running remote evaluation. You can run the previously listed commands with your own Dockerfile (in place of sk_smoke). This should produce a log of nontrivial metrics (evaluation is run on locally available minival).

To submit to the FALCON benchmark once your decoder Docker container is ready, follow the instructions on the EvalAI submission tab. This will instruct you to first install EvalAI, then add your token, and finally push the submission. It should look something like: evalai push mysubmission:latest --phase <phase-name> (dev or test) (Note that you will not see these instruction unless you have first created a team to submit. The phase should contain a specific challenge identifier. You may need to refresh the page before instructions will appear.)

Troubleshooting

Docker:

• If this is your first time with docker, note that sudo access is needed, or your user needs to be in the docker group. docker info should run without error.
• While sudo is sufficient for local development, the EvalAI submission step will ultimately require your user to be able to run docker commands without sudo.
• To do this, add yourself to the docker group. Note you may need vigr to add your own user.

EvalAI:

• pip install evalai may fail on python 3.11, see: https://github.com/aio-libs/aiohttp/issues/6600. We recommend creating a separate env for submission in this case.