paper-code

name	paper-code
description	Use when implementing experimental code, scaffolding research projects, or following coding best practices for academic work

Paper Code

Implement experimental code following best practices for reproducibility and academic standards.

Project Structure

project/
├── configs/           # Hyperparameters, settings
├── data/              # Raw and processed data
├── models/            # Model implementations
├── scripts/           # Training and evaluation scripts
├── notebooks/        # Exploratory analysis
├── tests/             # Unit and integration tests
├── logs/              # Experiment tracking
└── README.md          # Documentation

Code Quality Standards

Reproducibility

• Fixed random seeds (torch.manual_seed, numpy.random.seed)
• Config files for all hyperparameters
• Exact command reproduction in logs
• Environment specification (requirements.txt, conda env)

Style

• Clear variable and function names
• Comprehensive docstrings (Google/NumPy style)
• Type hints for function signatures
• Modular, reusable components

Testing

• Test core functions individually
• Validate data preprocessing pipelines
• Check metric calculations match paper definitions

Scripts

code-scaffold.py

Generate project templates for common experiment types:

• Classification experiments
• Generation experiments
• RL training loops
• Baseline implementations

References

• references/best-practices.md: Code quality standards and project structure

code-validator.py

Check code quality:

• Linting (flake8, black)
• Type checking (mypy)
• Import validation

Tips

• Start with a working baseline, then modify
• Keep experiment configs versioned
• Log everything (parameters, seeds, results)
• Use Weights & Biases or MLflow for tracking