docs/gpu_experiments/checkpoint_note.md

Checkpoint note — Models A vs B

Two existing checkpoints carry the SGT 1.1 M weights for this paper. They serve different purposes and must not be confused.

Model A — evaluation model (91/9 spatial split)

• Path: Weights-ResidualsModels-MappingInference-SOCmapping/ TemporalFusionTransformer/residualModels1mil_normalize_composite_l2_v2/ TFT_model_BEST_OVERALL_from_run_1_MAX_OC_150_TIME_BEGINNING_2007_ TIME_END_2023_TRANSFORM_normalize_LOSS_composite_l2_R2_0.6909.pth
• Trained on: ~15,128 samples (91% of 16,514)
• Held-out val: ~1,386 candidate samples; after the 1.2 km buffer that trims to 1,359 rows
• Confirmed val metrics: R² = 0.6258, RMSE = 4.758, MAE = 2.791, RPIQ = 1.051, val n = 1,359
• Bootstrap 95% CI (rebuttal/bootstrap_results.md): R² [0.526, 0.712], RMSE [4.18, 5.33]
• Architecture verified by checkpoint inspection: EnhancedSGT, 1,121,637 trainable parameters, BatchNorm2d in spatial encoder, LayerNorm in GRN blocks, 99 state-dict keys with module. prefix from Accelerate.
• Loss / transform actually used: LOSS_composite_l2 ≡ MSE (per the loss-collapse finding in rebuttal_numbers.md §1) + TRANSFORM_normalize (per-target standardisation).
• Purpose: Table 2 metrics in the paper. Used as the comparison row in kfold_results.md. NOT used for maps or uncertainty.

Model B — mapping model (full data)

• Path: Weights-ResidualsModels-MappingInference-SOCmapping/ TemporalFusionTransformer/finalResults2023_1milVersion_TRANSFORM_log_LOSS_l1/ TFT_model_BEST_OVERALL_from_run_1_MAX_OC_150_TIME_BEGINNING_2007_ TIME_END_2023_TRANSFORM_log_LOSS_l1_R2_1.0000.pth
• Trained on: all 16,514 samples (no held-out validation; use_validation = False in train.py, so the saved R² = 1.0000 in the filename is the placeholder, not a real metric)
• Architecture verified by checkpoint inspection: identical to Model A — EnhancedSGT, 1,121,637 parameters, same model_config block.
• Loss / transform actually used: L1 loss on torch.log(OC + 1e-10) target (TRANSFORM_log, LOSS_l1). Inverse at inference: np.exp(pred).
• Purpose: produced Figures 14/15 (2023 Bavaria SOC maps in the paper) and is the operational deployment model. Used by Experiment 2 (MC dropout uncertainty map). NOT used for Table 2 metrics.

Experiment 1 (spatial 5-fold CV)

• Uses neither A nor B as a starting point.
• Each of the 5 folds trains from scratch with the same recipe as Model B (EnhancedSGT, L1 loss on log + 1e-10 target, Adam @ lr=2e-4, 270 epochs, batch 256, dropout 0.3, per-fold feature normalisation fit on the in-fold training rows).
• The final row of kfold_results.md cites Model A's metrics (R² 0.626 / RMSE 4.758 / MAE 2.791 / RPIQ 1.051) for direct comparison only.

Experiment 2 (MC dropout uncertainty)

• Uses Model B only.
• Selective dropout activation: model.eval() then module.train() for every nn.Dropout (BatchNorm must stay in eval mode — population statistics).
• 30 stochastic forward passes per Bavaria-grid point; Welford accumulator tracks mean and variance in original SOC units (the log-transform is inverted via torch.exp inside the MC loop).
• Validation check: the MC mean should correlate with the single-pass Figures-14/15 prediction at Pearson r > 0.99 (sanity check, not a metric).