Contributing to Astrophage

Thank you for your interest in contributing! Astrophage is a Rust-based exoplanet classification project, and we welcome contributions of all kinds.

graph LR
    A[Fork] --> B[Branch]
    B --> C[Code]
    C --> D[Test]
    D --> E[PR]
    E --> F[Merge]

    style F fill:#2ecc71,stroke:#27ae60,color:#fff

Getting Started

Prerequisites

• Rust 1.85+ (install via rustup)
• Git

Setup

# Clone your fork
git clone https://github.com/YOUR_USERNAME/astrophage.git
cd astrophage

# Build
cargo build --release

# Run tests
cargo test

Development Workflow

graph TD
    A[Issue/Feature Request] --> B[Create Branch]
    B --> C[Implement]
    C --> D[Format & Lint]
    D --> E[Test]
    E --> F[Commit]
    F --> G[Push]
    G --> H[Pull Request]
    H --> I[Review]
    I -->|Approved| J[Merge]
    I -->|Changes| C

    style J fill:#2ecc71,stroke:#27ae60,color:#fff

Code Style

We follow standard Rust conventions:

# Format code
cargo fmt

# Run linter
cargo clippy

# Generate docs
cargo doc --open

Areas for Contribution

graph TB
    subgraph "High Priority"
        A1[Hyperparameter Tuning]
        A2[Cross-Validation]
        A3[Feature Selection]
    end

    subgraph "Medium Priority"
        B1[More Derived Features]
        B2[Model Serialization]
        B3[Prediction API]
    end

    subgraph "Low Priority"
        C1[Web Dashboard]
        C2[NASA Archive Integration]
        C3[GPU Acceleration]
    end

High Priority

1. Hyperparameter tuning — Grid search over tree depth, n_estimators, max_features
2. Cross-validation — K-fold stratified CV implementation
3. Feature selection — Recursive feature elimination to find optimal subset

Medium Priority

4. Additional derived features — More astrophysical interactions
5. Model serialization — Save/load trained models to avoid retraining
6. Prediction API — REST API for real-time classification

Low Priority

7. Web dashboard — Visualize predictions and feature importance
8. NASA Archive integration — Direct API connection for live data
9. GPU acceleration — CUDA kernels for tree training

Submitting Changes

sequenceDiagram
    participant C as Contributor
    participant R as Repo
    participant M as Maintainer

    C->>R: Fork repository
    C->>C: git checkout -b feature/amazing
    C->>C: git commit -m "Add amazing feature"
    C->>R: git push origin feature/amazing
    C->>R: Open Pull Request
    M->>R: Review code
    M->>C: Request changes (if needed)
    C->>R: Push updates
    M->>R: Approve & Merge

Pull Request Guidelines

• Describe what changed and why
• Reference any related issues
• Include test results
• Keep changes focused and atomic

Code of Conduct

• Be respectful and inclusive
• Focus on constructive feedback
• Help others learn
• Credit original authors

Questions?

Open an issue or reach out to @harihar-nautiyal.