This is an evolving repo optimized for machine-learning projects aimed at designing a new algorithm. They require sweeping over different hyperparameters, comparing to baselines, and iteratively refining an algorithm. Based of cookiecutter-data-science.

• src: contains main code for modeling (e.g. model architecture)
• experiments: code for runnning experiments (e.g. loading data, training models, evaluating models)
• scripts: scripts for hyperparameter sweeps (python scripts that launch jobs in experiments folder with different hyperparams)
• notebooks: jupyter notebooks for analyzing results and making figures
• tests: unit tests

• setup using uv (requires installing uv then run a script using uv run <script>).
• this installs a package named src for importing
  • see pyproject.toml for dependencies, not all are required
• example run: run uv run scripts/01_train_basic_models.py (which calls experiments/01_train_model.py) then view the results in notebooks/01_model_results.ipynb
• keep tests upated and run using uv run pytest

• scripts sweep over hyperparameters using easy-to-specify python code
• experiments automatically cache runs that have already completed
  • caching uses the (non-default) arguments in the argparse namespace
• notebooks can easily evaluate results aggregated over multiple experiments using pandas

• See some useful packages here
• Avoid notebooks whenever possible (ideally, only for analyzing results, making figures)
• Paths should be specified relative to a file's location (e.g. os.path.join(os.path.dirname(__file__), 'data'))
• Naming variables: use the main thing first followed by the modifiers (e.g. X_train, acc_test)
  • binary arguments should start with the word "use" (e.g. --use_caching) and take values 0 or 1
• Use logging instead of print
• Use argparse and sweep over hyperparams using python scripts (or custom things, like amulet)
  • Note, arguments get passed as strings so shouldn't pass args that aren't primitives or a list of primitives (more complex structures should be handled in the experiments code)
• Each run should save a single pickle file of its results
• All experiments that depend on each other should run end-to-end with one script (caching things along the way)
• Keep updated requirements in setup.py
• Follow sklearn apis whenever possible
• Use Huggingface whenever possible, then pytorch