examples.rst

Examples

This section provides a collection of examples demonstrating Hyperactive's capabilities. All examples are available in the examples directory on GitHub.

.. toctree::
   :maxdepth: 1

   examples/general
   examples/local_search
   examples/global_search
   examples/population_based
   examples/sequential_model_based
   examples/optuna_backend
   examples/sklearn_backend
    examples/skforecast_integration
   examples/integrations
   examples/other
   examples/interactive_tutorial

Overview

Hyperactive provides examples for all optimization algorithms and integration patterns. The examples are organized by algorithm category:

Gradient-Free Optimizers (GFO)

:ref:`examples_general`

Basic examples showing custom function optimization and sklearn model tuning.

:ref:`examples_local_search`

Hill Climbing, Simulated Annealing, Downhill Simplex, and other local search methods that explore by making incremental moves.

:ref:`examples_global_search`

Random Search, Grid Search, Powell's Method, and other algorithms that explore the search space more broadly.

:ref:`examples_population_based`

Particle Swarm, Genetic Algorithm, Evolution Strategy, and other nature-inspired population methods.

:ref:`examples_sequential_model_based`

Bayesian Optimization, Tree-Parzen Estimators, and other model-based methods that learn from previous evaluations.

Backend Examples

:ref:`examples_optuna_backend`

Examples using Optuna's samplers including TPE, CMA-ES, NSGA-II/III, and Gaussian Process optimization.

:ref:`examples_sklearn_backend`

Scikit-learn compatible interfaces as drop-in replacements for GridSearchCV and RandomizedSearchCV.

Integration Examples

:ref:`examples_integrations`

Time series optimization with sktime and other framework integrations.

:ref:`examples_skforecast_integration`

Forecasting hyperparameter tuning with SkforecastOptCV and ForecasterRecursive.

Advanced Topics

:ref:`examples_other`

Advanced usage patterns including warm starting and optimizer comparison.

:ref:`examples_interactive_tutorial`

Comprehensive Jupyter notebook tutorial covering all Hyperactive features.