Citrine v1.44.1 is released!

This release of Citrine Python introduces a few new capabilities as well as paving the way for new features coming in the Citrine Platform. Users can now set random seeds for Predictor Evaluation Workflows to ensure repeatability, create Design Spaces that are constrained to the bounds of their training data, and stream the contents of files on our platform directly. Additionally, we've introduced the first methods around the Experiment Data Source, which will be introduced in more detail in upcoming releases.

What's New

• We now allow Predictor Evaluation Workflows to be triggered with an optional random_state argument to pass a random seed to the evaluation method. This will allow users to set the random seed and ensure evaluation results are deterministic and reproducible. #788
• We have added the capability in the design_spaces.create_default() method to constrain parameters based on the predictor's training data. By passing include_parameter_constraints=True to the create_default method, process parameters will be constrained to the range of the training data in the resulting design space. #789
• We now have the ability to directly access the byte stream of a file_link via the read method. #790
• In preparation for upcoming platform features, we have added the ability to read Experiment Data Sources to a CSV format. An Experiment Data Source can be identified as an attribute of a specific Branch, or as part of the training_data of a Predictor. By calling the .read() method on the ExperimentDataSourceCollection resource and passing a UID or ExperimentDatasource object. The resulting information will allow you to inspect what is in the Data Source so you can verify, ingest, or perform additional analysis on your training data. We will include more documentation around how to interact with Experiment Data Sources in future releases. #787

Full Changelog: v1.41.1...v1.44.1