Energy + inelasticity regression#1
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… it, temp fix so I can run training wiht current files
…y); variance-weighted loss
eg huber loss, variable size binning, classification truth labels for upper-bound study. clarify labelling in eval plots and add history saving function for use by compare_runs notebook
… by upperbound truth label experiment results
…mprovements - Replace hardcoded 3-stack with nn.ModuleList (n_gravstack arg) - Add gradient accumulation to both trainers (--accumulation-steps) - Remove AMP/fp16 from regression trainer - Save model_config in checkpoint; eval notebook infers config from state_dict - Add per-class precision/recall/F1 to validation and eval notebook
…likelihood Model outputs (t1, alpha, beta); softplus enforces alpha/beta > 0 and alpha/(alpha+beta) is used as the point estimate at inference. Per-component t1/t2 losses tracked separately in metrics and logs.
- added binary and truth3b upper bound classifiers (train and evaluation scripts)
- added infrastructure for augmenting the regression model with binary probs
or the embeddings from the classifier
--vertex-dist appends ground-truth distance as extra input feature for upper-bound study --per-event-weighting to classifier - recompute class weights per batch rather than global ratio
- runs inference w/ classif. + regressor on val set - checks if failure modes overlap - produces overlap stats, correlations + comparison plots
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two-stage pipeline for energy and inelasticity regression (ve CC events)
for context, slides from mphys presentation :
slides-mphys.pdf