Development November 2022

[SebieF]

05.12.2022 - Version 0.2.1

Bug fixes

• Fixing loss function not working on GPU (Fixing loss device handling #62) (Already merged)
• Fixing incorrect metrics for classification task (Fixing metrics calculation in ClassificationSolver #63) (Already merged)
• Fixing path to string for pretrained model (=> path is correctly saved in out.yml)

Features

• Using device is now logged
• Adding a sanity_checker.py that checks if the test results have some obvious problems (like only predicting a single
  class) (wip)
• Adding a limited_sample_size flag to train the model on a subset of all training ids. Makes it easy to check if the
  model architecture is able to overfit on the training data
• Adding metrics from best training iteration to out.yml file (to compare with test set performance)
• Applying _validate_targets to all protocols in TargetManager
• Added a Changelog file

Maintenance

• Conversion dataset -> torch.tensor moved to embeddings.py
• Storing training/validation/test ids is replaced with the amount of samples in the respective sets
• Storing start and end time in a reproducible, readable format
• Export of ConfigurationException via init.py file for consistency
• Removing unnecessary double-loading of checkpoint for test evaluation
• Adding typing to split lists in TargetManager