spotoptim 0.0.5

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SpotOptim

Sequential Parameter Optimization with Bayesian Optimization.

Features

• Bayesian Optimization: Uses surrogate models to efficiently optimize expensive black-box functions
• Multiple Acquisition Functions: Expected Improvement (EI), Predicted Mean (y), Probability of Improvement (PI)
• Flexible Surrogates: Default Gaussian Process or custom Kriging surrogate
• Variable Types: Support for continuous, integer, and mixed variable types
• scipy-compatible: Returns OptimizeResult objects compatible with scipy.optimize

Installation

pip install spotoptim

Quick Start

import numpy as np
from spotoptim import SpotOptim

# Define objective function
def rosenbrock(X):
    X = np.atleast_2d(X)
    x, y = X[:, 0], X[:, 1]
    return (1 - x)**2 + 100 * (y - x**2)**2

# Set up optimization
bounds = [(-2, 2), (-2, 2)]

optimizer = SpotOptim(
    fun=rosenbrock,
    bounds=bounds,
    max_iter=50,
    n_initial=10,
    seed=42
)

# Run optimization
result = optimizer.optimize()

print(f"Best point: {result.x}")
print(f"Best value: {result.fun}")

Using Kriging Surrogate

SpotOptim includes a simplified Kriging (Gaussian Process) surrogate as an alternative to scikit-learn's GaussianProcessRegressor:

from spotoptim import SpotOptim, Kriging

# Create Kriging surrogate
kriging = Kriging(
    noise=1e-6,
    min_theta=-3.0,
    max_theta=2.0,
    seed=42
)

# Use with SpotOptim
optimizer = SpotOptim(
    fun=rosenbrock,
    bounds=bounds,
    surrogate=kriging,  # Use Kriging instead of default GP
    seed=42
)

result = optimizer.optimize()

API Reference

SpotOptim

Parameters:

• fun (callable): Objective function to minimize
• bounds (list of tuples): Bounds for each dimension as [(low, high), ...]
• max_iter (int, default=20): Maximum number of optimization iterations
• n_initial (int, default=10): Number of initial design points
• surrogate (object, optional): Surrogate model (default: GaussianProcessRegressor)
• acquisition (str, default='ei'): Acquisition function ('ei', 'y', 'pi')
• var_type (list of str, optional): Variable types for each dimension
• tolerance_x (float, optional): Minimum distance between points
• seed (int, optional): Random seed for reproducibility
• verbose (bool, default=False): Print progress information

Methods:

• optimize(X0=None): Run optimization, optionally with initial design points

Kriging

Parameters:

• noise (float, optional): Regularization parameter
• kernel (str, default='gauss'): Kernel type
• n_theta (int, optional): Number of theta parameters
• min_theta (float, default=-3.0): Minimum log10(theta) bound
• max_theta (float, default=2.0): Maximum log10(theta) bound
• seed (int, optional): Random seed

Methods:

• fit(X, y): Fit the model to training data
• predict(X, return_std=False): Predict at new points

Examples

See the notebooks/demos.ipynb for comprehensive examples including:

1. 2D Rosenbrock function optimization
2. 6D Rosenbrock with budget constraints
3. Using Kriging surrogate vs default GP

Development

# Clone repository
git clone https://github.com/sequential-parameter-optimization/spotoptim.git
cd spotoptim

# Install with uv
uv pip install -e .

# Run tests
uv run pytest tests/

# Build package
uv build

License

See LICENSE file.

References

Based on the SPOT (Sequential Parameter Optimization Toolbox) methodology.