用克里金模型优化昂贵黑箱函数，支持多类型变量与自动重启。
Optimization with SpotOptim
- 基于克里金模型与期望改进的优化循环，支持连续/整数/分类变量
- 通过OCBA实现噪声感知评估，成功率达90%以上时自动重启
- 兼容scikit-learn模型，可实时监控收敛过程，适合超参调优
spotoptim 包在 Python 中实现了基于代理模型的昂贵黑箱函数优化。基于二十年的顺序参数优化（SPO）方法，它提供基于克里金模型的优化循环，采用期望改进策略，支持连续、整数和分类变量，通过最优计算预算分配（OCBA）实现噪声感知评估，并具备多目标扩展。基于成功率的重启机制可在优化停滞时触发，同时保留已发现的最佳解。该包返回与 scipy 兼容的 OptimizeResult 对象，可接受任何 scikit-learn 兼容的代理模型。内置 TensorBoard 日志支持实时监控收敛性与代理模型质量。本文介绍 spotoptim 的架构与模块结构，提供包括神经网络超参数调优在内的完整示例，并与 BoTorch、Optuna、RayTune、BOHB、SMAC、Hyperopt 进行对比。该包为开源软件（AGPL-3.0）。
原文摘要 · Abstract (English)
The spotoptim package implements surrogate-model-based optimization of expensive black-box functions in Python. Building on two decades of Sequential Parameter Optimization (SPO) methodology, it provides a Kriging-based optimization loop with Expected Improvement, support for continuous, integer, and categorical variables, noise-aware evaluation via Optimal Computing Budget Allocation (OCBA), and multi-objective extensions. A success-rate-based restart mechanism detects stagnation while preserving the best solution found. The package returns scipy-compatible OptimizeResult objects and accepts any scikit-learn-compatible surrogate model. Built-in TensorBoard logging provides real-time monitoring of convergence and surrogate quality. This report describes the architecture and module structure of spotoptim, provides worked examples including neural network hyperparameter tuning, and compares the framework with BoTorch, Optuna, RayTune, BOHB, SMAC, and Hyperopt. The package is open-source (AGPL-3.0).
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