Emukit is a powerful Python toolkit that helps you make better decisions under uncertainty, especially when dealing with complex systems where data may be scarce. In this guide, we’ll walk you through the installation process, key features, and troubleshooting ideas — to ensure that you’re set up for success with Emukit.

What is Emukit?

Think of Emukit as having a personal assistant that helps you tackle intricate problems in decision-making. Just like a skilled guide who navigates through unknown terrains using compasses and maps (which represent various algorithms and data), Emukit employs multiple features to help you understand underlying uncertainties and optimize your outcomes.

Key Features of Emukit

• Multi-fidelity emulation: Create surrogate models using data from various sources offering different fidelity levels and associated costs.
• Bayesian optimisation: Enhance physical experiments and fine-tune machine learning algorithm parameters effectively.
• Experimental design/Active learning: Craft the most informative experiments and engage in active learning through machine learning models.
• Sensitivity analysis: Investigate how inputs impact outputs within a system.
• Bayesian quadrature: Calculate integrals of complex functions more efficiently.

Installation Process

Ready to dive in? Follow these straightforward steps for installation:

• Open your command line interface.
• Run the following command:

pip install emukit

If you want to explore additional installation options, feel free to check our documentation.

Dependencies

Emukit has a couple of prerequisites to ensure it’s working optimally:

• Numpy
• GPy

You can view more about these requirements in the requirements file.

Getting Started

To see Emukit in action, check out our tutorial notebooks for practical examples that will help you quickly grasp its functionality.

Documentation and Learning Resources

For an in-depth understanding of all that Emukit offers, refer to our full documentation. If you’re curious about the concept of emulation, take a tour of the Emukit playground.

Troubleshooting Tips

Common Issues and Solutions

• Installation Errors: If you encounter issues during installation, ensure that your pip is updated by running pip install --upgrade pip.
• Dependency Conflicts: Make sure that your essential packages, Numpy and GPy, are installed and compatible with your version of Python.
• Code Not Running as Expected: Double-check your code logic and compare it with examples from the tutorial notebooks.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Citing Emukit

If you find Emukit beneficial for your research, consider citing our work:

@inproceedings{emukit2019,
    author = {Paleyes, Andrei and Pullin, Mark and Mahsereci, Maren and McCollum, Cliff and Lawrence, Neil and González, Javier},
    title = {Emulation of physical processes with Emukit},
    booktitle = {Second Workshop on Machine Learning and the Physical Sciences, NeurIPS},
    year = {2019}
}

@article{emukit2023,
    title={Emukit: A Python toolkit for decision making under uncertainty},
    author={Andrei Paleyes and Maren Mahsereci and Neil D. Lawrence},
    journal={Proceedings of the Python in Science Conference},
    year={2023}
}

The papers can be found here: NeurIPS workshop 2019 and SciPy conference 2023.