# notebooks/

Training and evaluation notebooks for MLP and XGBoost models.

## 1. Files

| Notebook | Model | Description |
|----------|-------|-------------|
| `mlp.ipynb` | PyTorch MLP | Training, evaluation, and LOPO cross-validation |
| `xgboost.ipynb` | XGBoost | Training, evaluation, and LOPO cross-validation |

## 2. Structure (both notebooks)

Each notebook follows the same layout:

1. **Imports and CFG** — single config dict, project root setup
2. **ClearML (optional)** — opt-in experiment tracking
3. **Data loading** — uses `data_preparation.prepare_dataset` for consistent loading
4. **Random split training** — 70/15/15 stratified split with per-epoch/round logging
5. **Loss curves** — train vs validation loss plots
6. **Test evaluation** — accuracy, F1, ROC-AUC, classification report, confusion matrix
7. **Checkpoint saving** — model weights + JSON training log
8. **LOPO evaluation** — Leave-One-Person-Out cross-validation across all 9 participants
9. **LOPO summary** — per-person accuracy table + bar chart

## 3. Running

Open in Jupyter or VS Code with the Python kernel set to the project venv:

```bash
source venv/bin/activate
jupyter notebook notebooks/mlp.ipynb
```

Make sure the kernel's working directory is either the project root or `notebooks/` — the path resolution handles both.

## 4. Results

| Model | Random Split Accuracy | Random Split F1 | LOPO (mean) |
|-------|-----------------------|-----------------|-------------|
| XGBoost | 95.87% | 0.959 | see notebook |
| MLP | 92.92% | 0.929 | see notebook |