Release 0.5.3 · ITM-Kitware/align-system

0.5.3

Moved incontext learning functionality into incontext_utils.py and updated the base outlines and comparative regession ADMS to use this module.
Moved the format_choices() function from the OutlinesTransformersADM class in outlines_adm.py to a new utils file: adm_utils.py so it can be used across ADMs.
Update example_data/input_output_files to use DRE training scenarios
Changed default config to use outlines_transformers_structured_baseline (rather than the older single_kdma_baseline)
Adjusted choose_action() to enable returning an ADM-specific choice_info dictionary that is written to the resulting input_output.json file
When alignment target is optionally saved out in run_align_system save as JSON instead of YAML

Added option to normalize KDMA values in incontext examples
Added a probabilistic option to alignment utilities. Exposed this option in oracle, comparative regression, and
hybrid regression ADMs.
Example config for deterministic outlines-based ADM runs (align_system/configs/experiment/examples/outlines_force_determinism.yaml). Requires setting force_determinsim to true and using greedy sampler.
Added a history-based/cumulative KDE option to alignment utilities. Exposed this option in oracle and comparative regression.
Added true and predicted KDMA values to the log and input_output.json file for comparative regression ADM.
Added Phase 1 eval alignment targets for SoarTech

Fixed KDE target samples to be between 0 and 1
Fixed issue in alignment_utils logging (where kdma values can be a float/int rather than a list)
Now properly hydrating the meta_info field of input_output files
Fixed possible divide by zero during misaligned alignment
Properly hydrate Aid list