Bayesian Optimization — Project

This project demonstrates the implementation of Bayesian Optimization, a sample-efficient technique for optimizing expensive or unknown black-box functions.

Bayesian Optimization uses:

• A surrogate model (commonly a Gaussian Process) to approximate the objective function.
• An acquisition function to decide where to sample next (e.g., Expected Improvement, UCB).

---

Key Features

• Gaussian Process regression for function approximation.
• Multiple acquisition functions to guide exploration vs. exploitation.
• Example scripts demonstrating optimization of non-trivial functions.
• Visualizations for understanding the optimization process.

---

Example Use Case

# Example: Optimize a noisy 1D function
from bayes_opt import BayesianOptimization

def black_box_function(x):
    return -x**2 + 4

optimizer = BayesianOptimization(
    f=black_box_function,
    pbounds={'x': (-3, 3)},
    random_state=42
)

optimizer.maximize(
    init_points=2,
    n_iter=10
)

print(optimizer.max)

---

Requirements

• Python 3.x

• numpy

• scipy

• scikit-learn

• matplotlib (for visualization)

Install dependencies:

pip install -r requirements.txt

---

Learning Outcomes

• Understand how Gaussian Processes model unknown functions.

• Learn how acquisition functions balance exploration vs. exploitation.

• Gain experience applying Bayesian Optimization to real problems.

---

Attribution

This repository is a fork of the original bayesian-optimization project.

Forked to preserve, study, and showcase the implementation as part of my portfolio.