Examples#
This section provides a collection of examples demonstrating Hyperactive’s capabilities. All examples are available in the examples directory on GitHub.
Overview#
Hyperactive provides examples for all optimization algorithms and integration patterns. The examples are organized by algorithm category:
Gradient-Free Optimizers (GFO)#
- General Examples
Basic examples showing custom function optimization and sklearn model tuning.
- Local Search Algorithms
Hill Climbing, Simulated Annealing, Downhill Simplex, and other local search methods that explore by making incremental moves.
- Global Search Algorithms
Random Search, Grid Search, Powell’s Method, and other algorithms that explore the search space more broadly.
- Population-Based Algorithms
Particle Swarm, Genetic Algorithm, Evolution Strategy, and other nature-inspired population methods.
- Sequential Model-Based Algorithms
Bayesian Optimization, Tree-Parzen Estimators, and other model-based methods that learn from previous evaluations.
Backend Examples#
- Optuna Backend
Examples using Optuna’s samplers including TPE, CMA-ES, NSGA-II/III, and Gaussian Process optimization.
- Sklearn Backend
Scikit-learn compatible interfaces as drop-in replacements for GridSearchCV and RandomizedSearchCV.
Integration Examples#
- Integrations
Time series optimization with sktime and other framework integrations.
Advanced Topics#
- Advanced Examples
Advanced usage patterns including warm starting and optimizer comparison.
- Interactive Tutorial
Comprehensive Jupyter notebook tutorial covering all Hyperactive features.