How to Perform Bayesian Optimization with SciPy

SciPy itself doesn’t provide a dedicated Bayesian optimization function. However, you can use external libraries that work well with SciPy to perform Bayesian optimization. One such library is scikit-optimize (skopt), which integrates seamlessly with SciPy and offers Bayesian optimization capabilities.

Here’s an example of how to perform Bayesian optimization using scikit-optimize with SciPy:

Install scikit-optimize

pip install scikit-optimize

Use scikit-optimize for Bayesian Optimization

from skopt import BayesSearchCV
from sklearn.datasets import make_classification
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split

# Generate synthetic data for classification
X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_clusters_per_class=2, random_state=42)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define the parameter search space
param_space = {
    'C': (1e-6, 1e+6, 'log-uniform'),  # Example: Search for C in a log-uniform space
    'kernel': ['linear', 'rbf', 'poly'],  # Example: Search for different kernel types
    'degree': (1, 5),  # Example: Search for polynomial degree between 1 and 5
}

# Define the classifier
classifier = SVC()

# Perform Bayesian optimization using scikit-optimize
opt = BayesSearchCV(classifier, param_space, n_iter=50, n_jobs=-1, cv=5)
opt.fit(X_train, y_train)

# Print the best hyperparameters
print("Best Hyperparameters:", opt.best_params_)

In this example, we’re using BayesSearchCV from scikit-optimize to perform Bayesian optimization for hyperparameter tuning of a Support Vector Machine (SVM) classifier. You can customize the parameter search space and the classifier according to your specific use case.

Remember to check the latest documentation of scikit-optimize for any updates or changes in the library. The integration between scikit-optimize and SciPy allows you to leverage Bayesian optimization for hyperparameter tuning within the SciPy ecosystem.