Merge pull request #14 from automl/dev

Extended github pages.

Author: mwever

docs/api/games.md

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+
+::: hypershap.games

docs/api/hypershap.md

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+
+::: hypershap.hypershap

docs/api/surrogate_model.md

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+
+::: hypershap.surrogate_model

docs/api/task.md

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+
+::: hypershap.task

docs/api/utils.md

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+
+::: hypershap.utils

docs/authors.md

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+# Authors
+
+## Core Development
+
+- [Marcel Wever](https://www.marcelwever.de){target="_blank"}
+
+## Contributions
+- [Maximilian Muschalik](https://maxmuschalik.com/){target="_blank"}
+- [Fabian Fumagalli](https://fabianfumagalli.com/){target="_blank"}

docs/cite.md

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+# Cite HyperSHAP
+
+If you use HyperSHAP in your research, please cite the original paper:
+
+
+## Citation String
+Wever et al., (2025). HyperSHAP: Shapley Values and Interactions for Hyperparameter Importance. CoRR, abs/2502.01276, https://doi.org/10.48550/ARXIV.2502.01276.
+
+
+## Bibtex
+```bibtex
+@article{wever-arxiv25,
+  author       = {Marcel Wever and
+                  Maximilian Muschalik and
+                  Fabian Fumagalli and
+                  Marius Lindauer},
+  title        = {HyperSHAP: Shapley Values and Interactions for Hyperparameter Importance},
+  journal      = {CoRR},
+  volume       = {abs/2502.01276},
+  year         = {2025},
+  doi          = {10.48550/ARXIV.2502.01276},
+}
+```
+
+The paper introduces the underlying game-theoretic framework and demonstrates its usefulness for HPO explainability.

docs/contribute.md

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+# How to Contribute
+
+Contributions are welcome, and they are greatly appreciated!
+Every little bit helps, and credit will always be given.
+
+You can contribute in many ways:
+
+# Types of Contributions
+
+## Report Bugs
+
+Report bugs at https://github.com/automl/HyperSHAP/issues
+
+If you are reporting a bug, please include:
+
+- Your operating system name and version.
+- Any details about your local setup that might be helpful in troubleshooting.
+- Detailed steps to reproduce the bug.
+
+## Fix Bugs
+
+Look through the GitHub issues for bugs.
+Anything tagged with "bug" and "help wanted" is open to whoever wants to implement a fix for it.
+
+## Implement Features
+
+Look through the GitHub issues for features.
+Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it.
+
+## Write Documentation
+
+HyperSHAP could always use more documentation, whether as part of the official docs, in docstrings, or even on the web in blog posts, articles, and such.
+
+## Submit Feedback
+
+The best way to send feedback is to file an issue at https://github.com/automl/HyperSHAP/issues.
+
+If you are proposing a new feature:
+
+- Explain in detail how it would work.
+- Keep the scope as narrow as possible, to make it easier to implement.
+- Remember that this is a volunteer-driven project, and that contributions
+  are welcome :)
+
+# Get Started!
+
+Ready to contribute? Here's how to set up `HyperSHAP` for local development.
+Please note this documentation assumes you already have `uv` and `Git` installed and ready to go.
+
+1. Fork the `HyperSHAP` repo on GitHub.
+
+2. Clone your fork locally:
+
+```bash
+cd <directory_in_which_repo_should_be_created>
+git clone git@github.com:YOUR_NAME/HyperSHAP.git
+```
+
+3. Now we need to install the environment. Navigate into the directory
+
+```bash
+cd HyperSHAP
+```
+
+Then, install and activate the environment with:
+
+```bash
+uv sync
+```
+
+4. Install pre-commit to run linters/formatters at commit time:
+
+```bash
+uv run pre-commit install
+```
+
+5. Create a branch for local development:
+
+```bash
+git checkout -b name-of-your-bugfix-or-feature
+```
+
+Now you can make your changes locally.
+
+6. Don't forget to add test cases for your added functionality to the `tests` directory.
+
+7. When you're done making changes, check that your changes pass the formatting tests.
+
+```bash
+make check
+```
+
+Now, validate that all unit tests are passing:
+
+```bash
+make test
+```
+
+9. Before raising a pull request you should also run tox.
+   This will run the tests across different versions of Python:
+
+```bash
+tox
+```
+
+This requires you to have multiple versions of python installed.
+This step is also triggered in the CI/CD pipeline, so you could also choose to skip this step locally.
+
+10. Commit your changes and push your branch to GitHub:
+
+```bash
+git add .
+git commit -m "Your detailed description of your changes."
+git push origin name-of-your-bugfix-or-feature
+```
+
+11. Submit a pull request through the GitHub website.
+
+# Pull Request Guidelines
+
+Before you submit a pull request, check that it meets these guidelines:
+
+1. The pull request should include tests.
+
+2. If the pull request adds functionality, the docs should be updated.
+   Put your new functionality into a function with a docstring, and add the feature to the list in `README.md`.

docs/getting_started.md

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+# Getting Started
+
+## Installation
+
+The `HyperSHAP` package can be easily installed via PyPI:
+
+```bash
+pip install hypershap
+```
+
+Alternatively, one can clone the GitHub repository and install hypershap via the following command:
+
+```bash
+make install
+```
+
+## General Usage
+
+Given an existing setup with a ConfigurationSpace from the [ConfigSpace package](https://github.com/automl/ConfigSpace) and black-box function as follows:
+```Python
+from ConfigSpace import ConfigurationSpace, Configuration
+
+# ConfigurationSpace describing the hyperparameter space
+cs = ConfigurationSpace()
+  ...
+
+# A black-box function, evaluating ConfigSpace.Configuration objects
+def blackbox_function(cfg: Configuration) -> float:
+  ...
+```
+
+You can use HyperSHAP as follows:
+```Python
+from hypershap import ExplanationTask, HyperSHAP
+
+# Instantiate HyperSHAP
+hypershap = HyperSHAP(ExplanationTask.from_function(config_space=cs,function=blackbox_function))
+# Conduct tunability analysis
+hypershap.tunability(baseline_config=cs.get_default_configuration())
+# Plot results as a Shapley Interaction graph
+hypershap.plot_si_graph()
+```
+
+The example demonstrates how to:
+
+1. Wrap a black-box function in an explanation task.
+2. Use `HyperSHAP` to obtain interaction values for the **tunability** game.
+3. Plot the corresponding SI-graph.

docs/help.md

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+# Need help?
+
+Do you have further questions or a problem with `HyperSHAP` and do not know how to continue?
+
+1. Check the [examples](https://github.com/automl/HyperSHAP/tree/main/examples), maybe you can find a suitable solution.
+2. Check the [GitHub issues](https://github.com/automl/hypershap/issues), maybe someone else has been in a similar situation already.
+3. If you could not find anything related, please [create a help issue](https://github.com/automl/hypershap/issues).