UAVarPrior: Uncertainty-Aware Variational Prior

UAVarPrior is a framework for incorporating uncertainty-aware variational priors into machine learning models. This approach provides robust uncertainty quantification while maintaining predictive performance.

Features

• Variational Prior Integration: Leverage probabilistic modeling with variational priors
• Uncertainty Quantification: Capture and quantify both aleatoric and epistemic uncertainty
• Flexible Configuration: YAML-based configuration for reproducible experiments
• Command-line Interface: Intuitive CLI for training, evaluation, and inference
• Modern Model Architecture: Clean abstraction between model interfaces and implementations

🔗 Multi-Account Repository Access

This repository is synchronized across multiple GitHub accounts for enhanced collaboration and backup:

• Primary: SanjeevaRDodlapati/UAVarPrior
• Mirror 1: sdodlapati3/UAVarPrior
• Mirror 2: sdodlapa/UAVarPrior

All repositories are kept in perfect sync. Clone from any account you have access to.

Installation

# Clone from any of the synchronized repositories
git clone git@github.com:SanjeevaRDodlapati/UAVarPrior.git
# OR: git clone git@github.com:sdodlapati3/UAVarPrior.git
# OR: git clone git@github.com:sdodlapa/UAVarPrior.git
cd UAVarPrior

# Regular installation
pip install -e .

# For development (installs development dependencies)
pip install -e ".[dev]"

Migration from Legacy Version

If you're migrating from an older version of UAVarPrior, run the cleanup script first:

python scripts/clean_installation.py

This script will clean up old installation files to avoid conflicts.

Quick Start

Training a Model

1. Create a configuration file (see examples in config_examples/)
2. Run the training command:

uavarprior run config_examples/example_config.yml

Validating a Configuration

Before running a full training job, you can validate your configuration:

uavarprior validate config_examples/example_config.yml

For more thorough validation including model initialization:

uavarprior validate config_examples/example_config.yml --dry-run

Debugging a Configuration

If you're having issues with your configuration, use the debug command:

uavarprior debug-config config_examples/example_config.yml

Configuration Guide

UAVarPrior uses YAML configuration files to define models, datasets, and operations. See CONFIG_GUIDE.md for detailed information on configuration structure and options.

Example Configuration

# Operations to perform
ops:
  - train
  - evaluate

# Output directory
output_dir: ./outputs/example_run

# Model configuration
model:
  class: SimpleConvModel
  classArgs:
    input_channels: 4
    conv_channels: [16, 32, 64]
    kernel_size: 3
    pool_size: 2
    dropout: 0.2
    linear_features: [128, 64]
    output_features: 1

# Data configuration
data:
  dataset_class: uavarprior.data.sequences.SequenceDataset
  batch_size: 64
  train_args:
    data_path: ./data/training_data.h5
    split: train
  val_args:
    data_path: ./data/validation_data.h5
    split: val

# Training configuration
training:
  epochs: 10
  lr: 0.001

Model Implementation

UAVarPrior provides a clean interface for implementing models. To create a custom model:

1. Create a PyTorch model class (nn.Module)
2. Implement the required factory functions: get_model(), criterion(), and get_optimizer()
3. Use your model in the configuration file

See the example model for a complete implementation.

Large Files Handling

This repository uses .gitignore to exclude large files that exceed GitHub's 100MB limit. Key large files are stored in designated outputs directories that are not tracked by Git.

Locations for Large Files

• Data Files: Large data files should be stored in uavarprior/data/outputs/
• Model Weights: Large model files should be stored in uavarprior/model/outputs/
• Analysis Results: Large analysis files (like pickle files) should be stored in uavarprior/interpret/outputs/

Utility Scripts

UAVarPrior includes several utility scripts to help with common tasks:

• verify_model.py: Verify that a model can be loaded and used
• test_config.py: Test a configuration file for validity

Project Structure

The project is organized into several modules:

• data: For data processing and management
• model: For model definitions and training
• interpret: For result analysis and interpretation
• analysis: For analyzing model predictions

Each module has a docs directory with documentation and an outputs directory for large files.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

This project is licensed under the terms of the license included in the repository.

🚀 Multi-Account Development Workflow

For Contributors and Maintainers

This repository uses an advanced multi-account synchronization system for enhanced collaboration:

Quick Development Workflow

# 1. Make your changes
git add .
git commit -m "Your descriptive commit message"

# 2. Push to all accounts simultaneously (if you have maintainer access)
./push_all.csh

# 3. Your changes are now live on all three GitHub accounts!

Setting Up Multi-Account Access (Maintainers Only)

If you're a maintainer with access to multiple accounts:

# The repository includes automated push scripts
# Individual repo push: ./push_all.csh (pushes to all 3 accounts)
# Global push: ~/push_all_repos.csh (pushes all genomic repos)

For External Contributors

# Fork any of the synchronized repositories
# Work on your fork normally
# Submit PRs to the primary repository (SanjeevaRDodlapati/UAVarPrior)

Repository Synchronization Details

• Automatic Sync: All commits are automatically synchronized across accounts
• Branch Consistency: All repositories use main as the primary branch
• Real-time Updates: Changes appear on all accounts within seconds
• Admin Access: Full administrative privileges across all synchronized accounts

🔧 SSH Authentication Setup

For seamless multi-account access, maintainers use SSH key-based authentication:

# Test connections (maintainers only)
ssh -T github-sanjeevar    # Tests SanjeevaRDodlapati access
ssh -T github-sdodlapati3  # Tests sdodlapati3 access  
ssh -T github-sdodlapa     # Tests sdodlapa access

🤝 Collaboration Guidelines

1. Pull Requests: Submit PRs to the primary repository (SanjeevaRDodlapati)
2. Issues: Report issues on any of the synchronized repositories
3. Discussions: Use GitHub Discussions on the primary repository
4. Releases: Releases are synchronized across all accounts

🛡️ Security & Branch Protection

All repositories have branch protection enabled:

• Require pull request reviews before merging
• Prevent direct pushes to main (except for maintainers)
• Automatic security scanning and dependency alerts
• Protection against large file uploads and force pushes

📊 Repository Health

• CI/CD: Automated testing via GitHub Actions
• Code Quality: Automated linting and formatting checks
• Security: Dependabot alerts and vulnerability scanning
• Documentation: Automatically updated across all accounts