In this release of Citrine Python, we are happy to announce additional configurability of Design Spaces that are created via the `create_default` method. The method, which creates a new Design Space based on a given Predictor configuration, now takes three optional flags: one each to constrain the design space ingredient amounts, label amounts, or ingredient-counts-per-label based on the shape of the training data.
This lets you build your default design space in a number of ways: you can start similar to your training data and expand, or start with a big design space and constrain smaller. All these flags are optional; omitting them will default to "False" resulting in an unconstrained design space. As always, we are also shipping minor bug fixes to keep you running smoothly.
What's New
* Add configurability flags to the `create_default` Design Space method. 767
Fixes
* Minor bug fixes and improvements. https://github.com/CitrineInformatics/citrine-python/pull/766
**Full Changelog**: https://github.com/CitrineInformatics/citrine-python/compare/v1.33.2...v1.34.0