{"id":26059898,"url":"https://github.com/eva-kaushik/gauoptx-code-sample","last_synced_at":"2025-03-08T13:28:52.819Z","repository":{"id":280780942,"uuid":"931420243","full_name":"Eva-Kaushik/GauOptX-Code-Sample","owner":"Eva-Kaushik","description":"GauOptX is a high-performance Bayesian optimization tool designed for complex, mixed-variable problems. It features adaptive kernel switching, batch optimization, and GPU scalability, making it ideal for applications like hyperparameter tuning and experimental design.","archived":false,"fork":false,"pushed_at":"2025-03-05T08:44:19.000Z","size":1540,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-05T09:36:30.699Z","etag":null,"topics":["artificial-intelligence-algorithms","bayesian-optimization","gauoptx","optimisation-model","statistics-modeling"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Eva-Kaushik.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-12T08:48:43.000Z","updated_at":"2025-03-05T08:44:22.000Z","dependencies_parsed_at":"2025-03-05T09:47:06.513Z","dependency_job_id":null,"html_url":"https://github.com/Eva-Kaushik/GauOptX-Code-Sample","commit_stats":null,"previous_names":["eva-kaushik/gauoptx-code-sample"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eva-Kaushik%2FGauOptX-Code-Sample","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eva-Kaushik%2FGauOptX-Code-Sample/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eva-Kaushik%2FGauOptX-Code-Sample/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eva-Kaushik%2FGauOptX-Code-Sample/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Eva-Kaushik","download_url":"https://codeload.github.com/Eva-Kaushik/GauOptX-Code-Sample/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242555904,"owners_count":20148795,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence-algorithms","bayesian-optimization","gauoptx","optimisation-model","statistics-modeling"],"created_at":"2025-03-08T13:28:52.341Z","updated_at":"2025-03-08T13:28:52.774Z","avatar_url":"https://github.com/Eva-Kaushik.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# GauOptX: Gaussian Process Optimization Framework\nThis repository is a collaborative effort of Eva Kaushik(kaushikeva0026@gmail.com) and Rohit Kaushik(kaushikrohit004@gmail.com) \n\n## Introduction\n\n**GauOptX** is a high-performance, research-driven optimization framework that leverages **Bayesian optimization with Gaussian processes (GPs)** for efficient global optimization. Unlike traditional optimization techniques, which rely on gradient-based methods or brute-force search, GauOptX intelligently balances exploration and exploitation to find optima with minimal function evaluations.\n\nBayesian optimization is particularly useful for expensive-to-evaluate functions, such as:\n- Hyperparameter tuning in deep learning and machine learning models.\n- Experimental design in physical sciences and engineering.\n- Automated decision-making in reinforcement learning.\n\nGauOptX provides a modular, extensible, and scalable implementation that supports **batch optimization, multi-fidelity optimization, and large-scale data handling.**\n\n--------------------------\n## Why Bayesian Optimization? The Science Behind It\n\nOptimization problems often involve **black-box functions**—functions where we lack an explicit mathematical formula, and evaluation is costly (e.g., computationally expensive simulations, real-world experiments). Traditional methods struggle in these scenarios due to:\n\n- **No Gradient Information**: Many real-world functions are non-differentiable or noisy, making gradient-based methods ineffective.\n- **Computational Cost**: Grid search and brute-force methods are infeasible in high-dimensional spaces.\n- **Local Minima Traps**: Many heuristic methods (e.g., hill climbing, simulated annealing) may converge to suboptimal solutions.\n\n**Bayesian optimization (BO)** mitigates these issues by using a probabilistic model (typically a **Gaussian process**) to approximate the function and guide sampling based on uncertainty.\n\n---------------------------\n### Gaussian Processes (GPs) as a Surrogate Model\n\nA **Gaussian process** is a non-parametric probabilistic model that provides a distribution over possible functions that fit observed data. Given a set of observations, a GP can predict the mean and variance at unobserved points, allowing us to make **informed decisions** about where to evaluate next.\n\nMathematically, a Gaussian process is defined as:\n\\[\n f(x) \\sim \\mathcal{GP} (m(x), k(x, x'))\n\\]\nwhere:\n- \\(m(x)\\) is the mean function (often set to zero for simplicity).\n- \\(k(x, x')\\) is the covariance/kernel function (e.g., **RBF kernel**, Matern kernel), which determines the smoothness and complexity of the function approximation.\n\nUsing the GP model, we choose the next evaluation point using an **acquisition function**, which balances exploration (sampling in uncertain regions) and exploitation (sampling near promising points).\n\n### Acquisition Functions\n\nGauOptX supports multiple acquisition functions, each with different trade-offs:\n- **Expected Improvement (EI)**: Prioritizes points that are likely to improve upon the current best solution.\n- **Upper Confidence Bound (UCB)**: Encourages exploration based on confidence intervals.\n- **Probability of Improvement (PI)**: Focuses on sampling points with a high probability of being better than the current optimum.\n- **Thompson Sampling**: Uses random samples from the posterior to guide search.\n\n--------------------------\n\n## Key Features\n\n- **Global Optimization**: Finds the global minimum of black-box functions efficiently.\n- **Batch Optimization**: Supports parallel evaluations for faster convergence.\n- **Multi-fidelity Optimization**: Integrates noisy and multi-resolution function evaluations.\n- **Machine Learning Applications**: Optimize hyperparameters with fewer training iterations.\n- **Custom Kernel Support**: Use built-in kernels or define your own covariance functions.\n- **Visualization Tools**: Built-in plotting for convergence analysis.\n\n## Installation\n\n### Using `pip`\nTo install GauOptX, simply use:\n```bash\npip install gauoptx\n```\n\n### Installing from Source\n1. Clone the repository:\n    ```bash\n    git clone https://github.com/YourUsername/GauOptX.git\n    cd GauOptX\n    ```\n2. Install in development mode:\n    ```bash\n    python setup.py develop\n    ```\n3. Install dependencies:\n    ```bash\n    pip install -r requirements.txt\n    ```\n----------------------------\n\n## Dependencies\n\nGauOptX requires the following libraries:\n- `GPy` (for Gaussian process modeling)\n- `numpy` and `scipy` (for numerical computations)\n- `matplotlib` (for visualization)\n\nOptional dependencies:\n- `pyDOE` (for experimental design strategies)\n- `sobol_seq` (for quasi-random sequence sampling)\n- `cma` (for evolutionary strategies)\n- `DIRECT` (for derivative-free optimization)\n\nInstall all required dependencies using:\n```bash\npip install -r requirements.txt\n```\n------------------------------\n\n## Usage Example\n\n```python\nfrom gauoptx import GauOptX\nfrom gauoptx.benchmarks import synthetic_function\n\n# Define the objective function\ndef objective_function(x):\n    return synthetic_function(x)\n\n# Initialize optimizer\noptimizer = GauOptX(domain=[{'name': 'var_1', 'type': 'continuous', 'domain': (0, 1)}])\n\n# Run optimization\nresults = optimizer.optimize(objective_function)\n\n# Display results\nprint(\"Optimal value found: \", results.x)\nprint(\"Objective value at optimum: \", results.fx)\noptimizer.plot_convergence()\n```\n-------------------------------\n\n## Real-World Applications\n\n1. **Hyperparameter Optimization**: Bayesian optimization has been widely used in optimizing deep learning models (e.g., tuning CNN/LSTM architectures).\n2. **Drug Discovery**: BO efficiently searches large chemical spaces for optimal molecular configurations.\n3. **Robotics**: GauOptX can optimize control policies in reinforcement learning applications.\n4. **Engineering Design**: Used for optimizing expensive simulation-based problems in aerospace, automotive, and energy sectors.\n\n--------------------------------\n## Contributing\n\nWe welcome contributions! Whether you’re fixing a bug, adding new functionality, or improving documentation, your help is greatly appreciated.\n\n### How to Contribute:\n1. Fork the repository.\n2. Create a branch for your feature or bug fix:\n    ```bash\n    git checkout -b feature-or-bug\n    ```\n3. Make your changes and commit them.\n4. Push to your branch:\n    ```bash\n    git push origin feature-or-bug\n    ```\n5. Submit a pull request.\n\n---------------------------------\n\n## Citation\n\nIf you use **GauOptX** in your research, please cite:\n```\n@article{Kaushik2025,\n  author = {Eva Kaushik and Rohit Kaushik},\n  title = {GauOptX: A Gaussian Process Optimization Framework},\n  journal = {Journal of Machine Learning Optimization},\n  year = {2025},\n}\n```\n----------------------------------\n\n## Contact\nFor support, contact the maintainer at `kaushikeva0026@gmail.com' or 'Kaushikrohit004@gmail.com' .\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feva-kaushik%2Fgauoptx-code-sample","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feva-kaushik%2Fgauoptx-code-sample","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feva-kaushik%2Fgauoptx-code-sample/lists"}