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ImpactConfig management improved and optimized - #27
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…pose the interface for recreating ImpactConfig state from the YAML snapshot
…6) to verify reconstructed ImpactConfig integrity and correctness
…pactConfig snapshots saved alongside a model checkpoint
…onfig_from_checkpoint() and @inject_impact_config_overrides() decorators to simplify and improve ImpactConfig flow/recreation
… and adjacent config snapshot to eval/plotting functions (in src/impact/main.py and scripts/plots.py)
…tConfig snapshot next to the best-performing model checkpoint
…ct' command to enforce the caller to explicitly provide on which split partition the evaluation should be done
… to reflect the correct name of the other (mentioned) decorator
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Closes #26
Changelog:
src/impact/pipelines/sasrec_training_pipeline.py: madeSASRecTrainingPipelinealways savesImpactConfigsnapshot next to the best-performing model checkpoint (for reproducibility and future reference).src/impact/cli.py: defined@inject_impact_config_from_checkpoint()and@inject_impact_config_overrides()decorators to simplify and improve ImpactConfig management and recreation for the functions relying on SASRecNBR snapshot (evaluation/final plotting).src/impact/config.py: addedfrom_yaml()method toImpactConfigto reconstruct the config from a YAML snapshot, implemented integrity verification via config schema version and hash (checksum) verification.src/impact/utils.py: implementedfind_sibling_config()function for automated locatingImpactConfigsnapshots saved alongside the best-performingSASRecNBRmodel checkpoints.src/impact/main.pyandscripts/plots.pyto use newly defined decorators.