results: contains resulting metrics of each of the models and in each unique seed (used for selecting forget set that model leverages for unlearning). More details in README.md file inside folder.
final_metrics: contains final metrics of each of the models, averaged across all seeds (used for selecting forget set that model leverages for unlearning). More details in README.md file inside folder
load_model.py: contains code for saving (after untarring) + loading the model after unlearning (in the FASRC cluster), obtaining model predictions + prediction logits, obtaining the forget set used for unlearning
metrics.py: code for generating metric results given model weights + predictions (and logits). Metrics created in this file: wasserstein distance on forget/test set predictions in output space, KL divergence of model weights in weight space, L_2 distance of model weights in weight space.
metric_analysis.py: contains primary code for generating plots of metrics obtained from models after unlearning.