# evaluation

Systematic evaluation scripts and generated reports. All evaluation uses Leave-One-Person-Out (LOPO) cross-validation over 9 participants (~145k samples) as the primary generalisation metric.

## Scripts

| Script | What it does | Runtime |
|--------|-------------|---------|
| `justify_thresholds.py` | LOPO threshold search (Youden's J) for MLP and XGBoost; geometric alpha grid search; hybrid w_mlp grid search | ~10-15 min |
| `feature_importance.py` | XGBoost gain importance + leave-one-feature-out LOPO ablation | ~20 min (full) |
| `grouped_split_benchmark.py` | Compares pooled random split vs LOPO on the same XGBoost config | ~5 min |

### Quick mode

Add `--quick` to reduce tree count for faster iteration:

```bash
python -m evaluation.grouped_split_benchmark --quick
python -m evaluation.feature_importance --quick --skip-lofo
```

### ClearML support

```bash
USE_CLEARML=1 python -m evaluation.justify_thresholds --clearml
```

Logs threshold search results, weight grid searches, and generated reports as ClearML artifacts.

## Generated reports

| Report | Contents |
|--------|----------|
| `THRESHOLD_JUSTIFICATION.md` | ML thresholds (MLP t*=0.228, XGBoost t*=0.280), geometric weights (alpha=0.7), hybrid weights (w_mlp=0.3), EAR/MAR physiological constants |
| `GROUPED_SPLIT_BENCHMARK.md` | Pooled (95.1% acc) vs LOPO (83.0% acc) comparison |
| `feature_selection_justification.md` | Domain rationale, XGBoost gain ranking, channel ablation results |

## Generated plots

All plots are in `plots/` and referenced by the generated reports.

### ROC curves (LOPO, 9 folds, 144k samples)

| Plot | Model | AUC | Optimal threshold |
|------|-------|-----|-------------------|
| ![MLP ROC](plots/roc_mlp.png) | MLP | 0.862 | 0.228 |
| ![XGBoost ROC](plots/roc_xgb.png) | XGBoost | 0.870 | 0.280 |

Red dots mark the Youden's J optimal operating points. Both thresholds fall well below 0.50 due to cross-person probability compression under LOPO.

### Confusion matrices

| MLP | XGBoost |
|-----|---------|
| ![MLP CM](plots/confusion_matrix_mlp.png) | ![XGBoost CM](plots/confusion_matrix_xgb.png) |

### Weight grid searches

| Geometric alpha search | Hybrid w_mlp search |
|----------------------|-------------------|
| ![Geo weights](plots/geo_weight_search.png) | ![Hybrid weights](plots/hybrid_weight_search.png) |

Geometric pipeline: face-dominant weighting (alpha=0.7) generalises best across participants.
Hybrid pipeline: low MLP weight (0.3) with strong geometric anchor gives the best LOPO F1 (0.841).

### Physiological distributions

| EAR distribution | MAR distribution |
|-----------------|-----------------|
| ![EAR](plots/ear_distribution.png) | ![MAR](plots/mar_distribution.png) |

EAR thresholds (closed=0.16, blink=0.21, open=0.30) and MAR yawn threshold (0.55) are validated against these distributions.

## Key findings

1. LOPO drops ~12 pp vs pooled split, confirming the importance of person-independent evaluation
2. Threshold optimisation alone yields +2-4 pp F1 without retraining
3. All three feature channels contribute (removing any one drops F1 by 2-10 pp)
4. `s_face` and `ear_right` are the highest-gain features, confirming that head pose and eye state are the strongest focus indicators
5. The geometric anchor (70% weight) stabilises the hybrid model against per-person variance

## Evaluation logs

Training logs (per-epoch CSVs and JSON summaries) are written to `logs/` by the MLP and XGBoost training scripts.