ProcessOptimizer 0.6.2

Sequential model-based optimization toolbox (forked from scikit-optimize)

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This readme.md is work in progress

Table of Contents

• ProcessOptimizer
• Installation
• Contributions
• Related work
• PyPi

ProcessOptimizer

ProcessOptimizer is a fork of scikit-optimize. ProcessOptimizer will fundamentally function like scikit-optimize, yet developments are focussed on bringing improvements to help optimizing real world processes, like chemistry or baking. For examples on use, checkout https://github.com/novonordisk-research/ProcessOptimizer/tree/develop/examples.

Installation

ProcessOptimizer can be installed using pip install ProcessOptimizer The repository and examples can be found at https://github.com/novonordisk-research/ProcessOptimizer ProcessOptimizer can also be installed by running pip install -e . in top directory of the cloned repository.

Contributions

Feel free to play around with algorithm. Should you encounter errors while using ProcessOptimizer, please report them at https://github.com/novonordisk-research/ProcessOptimizer/issues.
To help solve the issues, please:

• Provide minimal amount of code to reproduce the error
• State versions of ProcesOptimizer, sklearn, numpy, ...
• Describe the expected nehavior of the code
  

If you would like to contribute by making anything from documentation to feature-additions, THANK YOU. Please open a pull request marked as WIP as early as possible and describe the issue you seek to solve and outline your planned solution.

Related work

We are currently building a GUI to offer the power of Bayesian Process Optimization to non-coders. Stay tuned.

PyPi

If you have not packaged before check out https://packaging.python.org/tutorials/packaging-projects/ To upload a new version to PyPi do the following in the root folder of the project:

• In terminal run the command "pytest" and make sure there are no errors
• Change version number in setup.py
• Change version number in ProcessOptimizer/__init__.py
• Remember to pip install twine if running in a new virtual env
• Run python setup.py sdist bdist_wheel
• Run python -m twine upload dist/* (make sure that /dist only contains relevant version)