dynamic-tuna 0.1.0

A description of what dynamic_tuna does

Dynamic Tuna: Flexible Bayesian Optimization Library

Dynamic Tuna is a Bayesian optimization library built on Optuna and scikit-optimize that supports customizable exploration-exploitation control. The supported samplers (surrogate models) are Gaussian Process, Random Forest, Gradient Boosted Decision Trees, and Tree-structured Parzen Estimator -- each with customizable exploration-exploitation controls.

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🌟 Features

• Dynamic Sampling: Control the exploration-exploitation tradeoff with flexible n_ei_function parameters.

• Diverse Samplers: Use Gaussian Process, Random Forest, or Gradient Boosted Trees as the surrogate model for efficient sampling.

• Customizable Hyperparameters: Define custom n_ei_function parameters for precise control over the optimization process.

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📥 Installation

Clone the repository and install the required packages from requirements.txt:

git clone https://github.com/yourusername/dynamic_tuna.git
cd dynamic_tuna
pip install -r requirements.txt


	Note: This library requires Python 3.8+.

🚀 Getting Started

Here’s a quick example to get you started with Dynamic Tuna.


import optuna
from dynamic_tuna import GBTSampler

# Define search space
search_space = {
    "param1": optuna.distributions.UniformDistribution(0, 1),
    "param2": optuna.distributions.IntUniformDistribution(1, 100)}

# Initialize sampler with dynamic EI candidates
sampler = GBTSampler(search_space, n_ei_function=lambda n, a=1, b=2: a * n + b)

# Create and run study
study = optuna.create_study(sampler=sampler)
study.optimize(objective_function, n_trials=50)
print("Best Parameters:", study.best_params)

⚙️ Usage

1.	Initialize a Sampler:

Choose from GBTSampler, RandomForestSampler, or GPSampler and define your n_ei_function. 2. Define a Search Space: Use Optuna’s UniformDistribution, IntUniformDistribution, and more. 3. Run Optimization: Run your study with optuna.create_study() and check the results.

🔧 Contributing

Contributions are welcome! To contribute:

1.	Fork the repository.
2.	Create a new branch with your feature or bugfix.
3.	Submit a pull request with a description of your changes.

Please ensure that your code is well-documented and tested.

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

📬 Contact

Have questions or feedback? Reach out to me at [e-mail] or create an issue in the repository.

Happy Optimizing! 🎉