+
+
+
+
+
+ Hyperactive
+A unified interface for optimization algorithms and experiments
+
+
+
+
+Hyperactive provides a collection of optimization algorithms, accessible through a unified
+experiment-based interface that separates optimization problems from algorithms. The library
+provides native implementations of algorithms from the Gradient-Free-Optimizers package
+alongside direct interfaces to Optuna and scikit-learn optimizers.
+
+.. raw:: html
+
+
+
+
+----
+
+.. _features:
+
+Features
+================
+
+What makes Hyperactive stand out for optimization tasks.
+
+.. grid:: 1 2 3 3
+ :gutter: 4
+
+ .. grid-item-card::
+ :class-card: feature-card
+
+ **20+ Optimization Algorithms**
+ ^^^
+ From Hill Climbing to Bayesian Optimization,
+ Particle Swarm, Genetic Algorithms, and more.
+
+ +++
+ :doc:`Local, global, population-based, and sequential methods
+
+ 31
+ Algorithms
+
+
+ 3
+ Backends
+
+
+ 5
+ Integrations
+
+
+ 1
+ Unified API
+ 
+ 
+ 
+ 
+
+
+
+
+----
+
+Quick Example
+=============
+
+Get started in just a few lines of code:
+
+.. raw:: html
+
+
+
+
+
+
+ $ pip install hyperactive
+
+
+ $ pip install hyperactive[all_extras]
+
+
+ $ pip install hyperactive[sklearn-integration]
+
+
+ $ pip install hyperactive[sktime-integration]
+
+
+
+
+
+----
+
+Contents
+========
+
+.. toctree::
+ :maxdepth: 1
+ :hidden:
+
+ get_started
+ installation
+ user_guide
+ api_reference
+ examples
+ faq
+ troubleshooting
+ get_involved
+ about
+
+.. raw:: html
+
+
+
+
+
+.. literalinclude:: _snippets/getting_started/index_custom_function.py
+ :language: python
+ :start-after: # [start:full_example]
+ :end-before: # [end:full_example]
+
+.. raw:: html
+
+
+
+
+.. literalinclude:: _snippets/getting_started/index_sklearn_tuning.py
+ :language: python
+ :start-after: # [start:full_example]
+ :end-before: # [end:full_example]
+
+.. raw:: html
+
+
+
+
+.. literalinclude:: _snippets/getting_started/index_bayesian.py
+ :language: python
+ :start-after: # [start:full_example]
+ :end-before: # [end:full_example]
+
+.. raw:: html
+
+
+
+
+
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
new file mode 100644
index 00000000..75554816
--- /dev/null
+++ b/docs/source/installation.rst
@@ -0,0 +1,205 @@
+.. _installation:
+
+============
+Installation
+============
+
+Hyperactive can be installed via pip and supports Python |python_version_range|.
+
+Installing Hyperactive
+----------------------
+
+Basic Installation
+^^^^^^^^^^^^^^^^^^
+
+Install Hyperactive from PyPI using pip:
+
+.. code-block:: bash
+
+ pip install hyperactive
+
+This installs Hyperactive with its core dependencies, which is sufficient for most use cases
+including scikit-learn integration.
+
+
+Installation with Extras
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+For additional functionality, you can install optional extras:
+
+.. code-block:: bash
+
+ # Full installation with all extras (Optuna, PyTorch Lightning, etc.)
+ pip install hyperactive[all_extras]
+
+ # Scikit-learn integration (included by default)
+ pip install hyperactive[sklearn-integration]
+
+ # Sktime/skpro integration for time series
+ pip install hyperactive[sktime-integration]
+
+
+Development Installation
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+To install Hyperactive for development (from source):
+
+.. code-block:: bash
+
+ # Clone the repository
+ git clone https://github.com/SimonBlanke/Hyperactive.git
+ cd Hyperactive
+
+ # Install in development mode with test dependencies
+ pip install -e ".[test]"
+
+ # Or install with all development dependencies
+ pip install -e ".[test,docs]"
+
+
+Dependencies
+------------
+
+Core Dependencies
+^^^^^^^^^^^^^^^^^
+
+Hyperactive requires the following packages (automatically installed):
+
+.. list-table::
+ :header-rows: 1
+ :widths: 30 70
+
+ * - Package
+ - Purpose
+ * - ``numpy >= 1.18.1``
+ - Numerical operations and array handling
+ * - ``pandas < 3.0.0``
+ - Data manipulation and results handling
+ * - ``tqdm >= 4.48.0``
+ - Progress bars during optimization
+ * - ``gradient-free-optimizers >= 1.2.4``
+ - Core optimization algorithms
+ * - ``scikit-base < 1.0.0``
+ - Base classes for sklearn-like interfaces
+ * - ``scikit-learn < 1.8.0``
+ - Machine learning integration
+
+
+Optional Dependencies
+---------------------
+
+Depending on your use case, you may want to install additional packages:
+
+Optuna Backend
+^^^^^^^^^^^^^^
+
+For Optuna-based optimizers (TPE, CMA-ES, NSGA-II, etc.):
+
+.. code-block:: bash
+
+ pip install optuna
+
+Or include it via the ``all_extras`` option:
+
+.. code-block:: bash
+
+ pip install hyperactive[all_extras]
+
+
+Time Series (sktime/skpro)
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+For time series forecasting and probabilistic prediction:
+
+.. code-block:: bash
+
+ pip install hyperactive[sktime-integration]
+
+This installs ``sktime`` and ``skpro`` for time series optimization experiments.
+
+.. note::
+
+ Sktime integration requires Python < 3.14 due to sktime's current compatibility.
+
+
+PyTorch Lightning
+^^^^^^^^^^^^^^^^^
+
+For deep learning hyperparameter optimization:
+
+.. code-block:: bash
+
+ pip install hyperactive[all_extras]
+ # or
+ pip install lightning
+
+.. note::
+
+ PyTorch/Lightning requires Python < 3.14 for full compatibility.
+
+
+Verifying Installation
+----------------------
+
+After installation, verify that Hyperactive is working correctly:
+
+.. literalinclude:: _snippets/installation/verify_installation.py
+ :language: python
+ :start-after: # [start:verify_installation]
+ :end-before: # [end:verify_installation]
+
+
+Python Version Support
+----------------------
+
+Hyperactive officially supports Python |python_versions_list|.
+
+.. note::
+
+ The supported Python versions are automatically extracted from the project's
+ ``pyproject.toml`` classifiers.
+
+ Some optional integrations (sktime, PyTorch) may have more restrictive
+ Python version requirements. Check the specific package documentation
+ for details.
+
+
+Troubleshooting
+---------------
+
+Common Installation Issues
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+**ImportError: No module named 'gradient_free_optimizers'**
+
+This usually means the installation was incomplete. Try:
+
+.. code-block:: bash
+
+ pip install --upgrade hyperactive
+
+**MemoryError during optimization**
+
+Sequential model-based optimizers (Bayesian, TPE) can use significant memory
+for large search spaces. Reduce your search space size or use a simpler optimizer
+like ``RandomSearch`` or ``HillClimbing``.
+
+**Pickle errors with multiprocessing**
+
+Ensure all objects in your search space are serializable (no lambdas, closures,
+or bound methods). Use top-level functions and basic Python types.
+
+For more help, see the `GitHub Issues
+
+
+
+
+
+ Get Started
+ Quick introduction to Hyperactive
+ →
+
+
+
+
+
+
+ Installation
+ Setup guide and requirements
+ →
+
+
+
+
+
+
+ User Guide
+ In-depth tutorials and explanations
+ →
+
+
+
+
+
+
+ API Reference
+ Technical reference for all classes
+ →
+
+
+
+
+
+
+ Examples
+ Code examples and use cases
+ →
+
+
+
+
+
+ Get Involved
+ Contribute to Hyperactive
+ →
+