# Phase 3: Model Training Pipeline - Completion Report

**Date**: 2026-04-25
**Status**: Phase 3 infrastructure COMPLETE, training pending

---

## Deliverables Status

### 3.1 PyTorch Geometric Spatiotemporal Model ✓
- **File**: `models/spatiotemporal_gcn/model.py`
- **Architecture**:
  - Transformer encoder: 3 layers, 4 heads, dim=48, ff_dim=192, dropout=0.2
  - GCN: GCNConv(48, 128) → ReLU → Dropout → GCNConv(128, 64)
  - Output: [N, 3] for 1-day, 3-day, 7-day risk
- **ONNX Export**: `models/spatiotemporal_gcn/model_1_3_7.onnx`
- **Verified**: Forward pass works on GPU

### 3.2 GraphSAINT Sampler ✓
- **File**: `models/spatiotemporal_gcn/sampler.py`
- **Config**: Layer depths [256, 128, 64], batch_size=256
- **Compatibility**: Works with base PyG (no torch-sparse required)
- **Verified**: Sampler produces valid mini-batches

### 3.3 MLflow Tracking Server ✓
- **File**: `deploy/docker-compose.mlflow.yml`
- **Services**: MLflow server + PostgreSQL with PostGIS
- **Endpoint**: http://localhost:5000
- **Status**: Docker compose file created

### 3.4 Baseline MAE Computation ✓
- **File**: `scripts/compute_baseline_mae.py`
- **Results** (validation set: 2023-07-01 to 2024-12-30):

| Horizon | Baseline MAE | Target (<0.9x) |
|---------|--------------|-----------------|
| 1-day   | 0.2314       | < 0.2083        |
| 3-day   | 0.5424       | < 0.4882        |
| 7-day   | 0.6391       | < 0.5752        |

- **Report**: `reports/baseline_mae.md`

### 3.5 Training Run ✓
- **File**: `scripts/train_model.py`
- **Verified**: Data loading works (140k nodes, 23 stations, 9k medical records)
- **Configuration**:
  - Learning rate: 1e-4
  - Weight decay: 0.01
  - Patience: 15
  - Max epochs: 200
  - Batch size: 1024
- **Status**: Ready to run training

### 3.6 Lambda Smooth Tuning ⏸️
- **Status**: Not yet implemented
- **Plan**: Search over [0.01, 0.05, 0.1, 0.2, 0.5]

### 3.7 ONNX Export ✓
- **Status**: Already included in model.py
- **Exported**: `models/spatiotemporal_gcn/model_1_3_7.onnx`

### 3.8 Evaluation on Test Set ⏸️
- **Status**: Pending - requires training to complete first

---

## Environment Verification

| Component | Status | Notes |
|-----------|--------|-------|
| PyTorch | ✓ | 2.10.0+cu128 |
| CUDA | ✓ | 12.8, RTX 3050 4GB |
| PyG | ✓ | 2.7.0 |
| Model | ✓ | Forward pass OK |
| Sampler | ✓ | Mini-batch OK |
| MLflow | ✓ | 3.11.1 installed |
| ONNX | ✓ | 1.21.0, Runtime 1.25.0 |

**GPU Memory**: 4GB VRAM (RTX 3050) - sufficient with GraphSAINT sampling

---

## To Start Training

```bash
# Start MLflow (if not running)
docker-compose -f deploy/docker-compose.mlflow.yml up -d

# Run training
python scripts/train_model.py
```

---

## Next Steps

1. **Run training**: `python scripts/train_model.py`
   - Expected time: Several hours on 4GB GPU
   - Monitor via MLflow UI at http://localhost:5000

2. **After training completes**:
   - Implement Phase 3.6 (Lambda smooth tuning)
   - Run Phase 3.8 (evaluation on test set)

3. **Proceed to Phase 4** (Inference Pipeline)