GSoC Week 5 & 6: Posterior Refactoring and Design Improvements

Over the past two weeks, my work on sbi focused on refining the build_posterior API, improving consistency across trainer classes, and contributing both code and design discussions.

Refining the build_posterior API

I started by addressing comments on my earlier PR that introduced the build_posterior API (PR #1601).

• Added unit tests to ensure that invalid density estimator values raise the appropriate errors.
• Refactored some functions into helper methods to make the API cleaner and easier to extend.
• Reorganized the order of inference class methods to follow best practices and improve readability.

Code Review Contributions

I reviewed a PR opened by one of the maintainers (PR #1615) and provided feedback on type consistency and potential side effects.

• Suggested adding an assertion to catch a type mismatch flagged by the type checker.
• Recommended consistent use of .numpy() in the regression_model.fit function.
• Raised a question about an sbi function whose parameters take a Tensor, and whether modifying the original tensor passed to the function was intended behavior.

To strengthen my feedback, I prepared and shared a reproducible Colab demo: Notebook Link.

Ensuring Consistency Across Trainers

To align with recent naming updates, I opened a PR renaming VectorFieldInference to VectorFieldTrainer (PR #1614) for consistency with other trainer classes.

Exploring the Factory Pattern

During the previous week, I had implemented a factory-pattern-based class for handling posterior creation. After discussing it with my mentors, we decided the added abstraction may not be necessary at this stage. However, it could be useful in the future. To capture this work, I wrote and shared a design document with the sbi team describing the factory method approach.

Extending the Refactored API

In Week 6, I extended the refactored build_posterior API to the FMPE and NPSE trainer classes, making them consistent with other trainers. I also updated the related tests to reflect this change.

Improving Parameter Handling

With the refactored build_posterior now centralized, I worked on integrating it with the posterior parameter dataclasses developed in earlier weeks.

• Updated the resolution logic to handle values passed via both dictionaries and dataclasses.
• Enhanced the VectorFieldPosteriorParameters dataclass to include fields required for building VectorFieldPotential (previously hidden behind keyword arguments).
• Improved the resolution logic to issue clear warnings when conflicting values are provided from multiple parameters and added unit tests to ensure the logic works properly for VIPosterior and MCMCPosterior.

These two weeks were productive in terms of both implementation and design discussions. I feel the build_posterior API is now in a much stronger and more consistent state, and I’m glad to have also contributed design documentation that can serve as a reference for future iterations.