New SpotOptim Python package released for black-box function optimization

The SpotOptim Python package has been released, offering a framework for optimizing expensive black-box functions. It utilizes a Kriging-based approach with Expected Improvement and supports various variable types, noise-aware evaluations, and multi-objective optimization. The package includes features like a success-rate-based restart mechanism to prevent stagnation and integrates with scikit-learn compatible surrogate models. SpotOptim also provides TensorBoard logging for real-time monitoring and is compared against several other popular optimization tools. AI

IMPACT Provides a new tool for optimizing machine learning hyperparameters and other expensive black-box functions.

RANK_REASON The item is a research paper describing a new software package for optimization. [lever_c_demoted from research: ic=1 ai=1.0]

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• BoTorch
• Hyperopt
• Kriging
• Optimal Computing Budget Allocation
• Optuna
• Python
• RayTune
• scikit-learn
• SpotOptim
• TensorBoard

• paper
• product
• infra

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