# GMM Implementation Status

## Completed Components

1. **Core Modules**:
   - ✅ `config`: Configuration system for experiments
   - ✅ `core`: Core model architecture (transformer blocks)
   - ✅ `metrics`: Metrics tracking and calculation
   - ✅ `training`: Trainer implementation and experiment management
   - ✅ `utils`: Checkpointing and visualization utilities
   - ✅ `data`: Data generation and loading (as submodule)
   - ✅ `mi`: Mutual information estimation (as submodule)
   - ✅ `losses`: Loss functions including MSE, Wasserstein, and Energy distance

2. **Scripts**:
   - ✅ `runners`: Experiment runners with config presets
   - ✅ `evaluation`: Model evaluation scripts
   - ✅ `check_imports.py`: Tool to verify all module imports are working

3. **Configuration Files**:
   - ✅ `configs/data`: Data generation configuration
   - ✅ `configs/model`: Model architecture presets
   - ✅ `configs/training`: Training process configuration
   - ✅ `configs/validation`: Validation and evaluation configuration

## Integration Process

The integration process has successfully integrated all components from the old repository (`gmm_v2_old`) into the new modular structure, with the data and mi components as Git submodules.

All import paths have been updated to use absolute imports, and the functionality has been preserved while improving code organization.

### Loss Functions Integration

The losses module has been successfully integrated with the following components:
- ✅ MSE loss for direct point-to-point comparisons
- ✅ Energy distance-based losses for distribution comparison
- ✅ Wasserstein distance with multiple backend implementations:
  - Exact solver using scipy or POT
  - Regularized Sinkhorn solver using POT
  - JAX acceleration (optional)
- ✅ Unified interface with factory functions

The metrics registry has been updated to use the loss functions directly from the losses module.

## Running Experiments

Basic experiments can be run using the following command:

```bash
python scripts/runners/simple_experiment_runner.py --model-preset small --training-preset quick
```

Cross-evaluation can be performed using:

```bash
python scripts/evaluation/evaluate_gmm_model.py --model-path runs/my_experiment/model_best.pt --data-preset standard
```

An integrated example that demonstrates all components working together:

```bash
python main.py
```

## Verify Installation

To verify that all modules are properly installed and accessible:

```bash
python check_imports.py
```

This will check all key module imports and report any issues.

## Next Steps

1. Add more comprehensive tests 
2. Improve documentation
3. Create additional tutorials
4. Integrate more advanced MI estimation methods

## Implementation Challenges and Solutions

### Import Path Resolution
Problem: Python path resolution with submodules required careful path handling.
Solution: Used absolute imports and explicit path addition to sys.path in scripts.

### Module Dependencies
Problem: Each module had dependencies on others that needed to be managed.
Solution: Used clear import structure and conditional imports where needed.

### Loss Function Integration
Problem: Wasserstein implementations required multiple backends with different capabilities.
Solution: Created a unified factory interface with backend selection based on availability and requirements.

### Configuration System Complexity
Problem: Configuration system had deep nesting and scattered configuration classes.
Solution: Reorganized configuration classes into focused modules and added helper properties for direct access to nested settings.

### Wasserstein Loss Implementation
Problem: Wasserstein loss had silent fallbacks that could hide issues.
Solution: Removed automatic fallbacks, improved error messages, and enhanced point expansion logic.

## Notes for Future Development
- Consider adding more visualization options
- Expand MI estimation methods
- Add more examples in the tutorials directory
- Implement additional model architectures beyond transformers
- Add support for more loss functions (e.g., JS divergence, MMD, etc.)