This repo contains the code for the Personalization up to a point paper.
Directory: modules/
Modules provide the basic functions for data and model manipulation, with no entrypoint.
Directory: notebooks/
Notebooks provide entrypoints for all the phases of the project (data exploration, dataset building, model training, evaluation). The three main ones are:
build_stratified_dataset.ipynb: builds the stratified train-test split for the DTC (also called "Kumar") dataset. The resulting splits are then used for model training.majority_vote_model.ipynb: trains the majority vote (Maj) model.sepheads_model_training.ipynb: trains the personalized SepHeads (Sep) model.
Directory: scripts/
Scripts for model training, equivalent to the corresponding notebooks but in a form runnable through e.g. on a cluster via a job scheduler.
MIT License
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