- Compute AP (Mean Reciprocal Rank) in _evaluate() for both q→g and g→q
- AP saved in val.csv and train_recall.csv alongside R@K
- New AP plot panel in val_metrics.png (train vs val, both directions)
- Log AP in console output for train-recall and val epochs
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- _evaluate() now computes per-batch loss when loss_fn is provided
- Val loss and train recall loss saved in val.csv and train_recall.csv
- Overview plot shows train vs val loss curves side by side
- Helps detect overfitting: val loss diverging from train loss
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Evaluate R@K on train set (subset matching test size) alongside val
- New train_recall.csv with per-epoch train R@1/R@5/R@10
- Plot train vs val recall on same chart (solid=val, dashed=train)
- Helps detect overfitting: train R@1 up + val R@1 flat = overfit
- Train eval uses clean transforms (no augmentation)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New: src/training/plot_metrics.py
- train_metrics.png: loss, temperature, gates, lr
- val_metrics.png: R@K q→g and g→q
- overview.png: combined loss + R@1 + gates/tau
Auto-generates plots in {output_dir}/logs/ after each validation epoch.
Also callable standalone: python -m src.training.plot_metrics --log-dir ...
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
