GSoC Week 7 & 8: Posterior Parameters with Dataclasses and Validation

Over the past two weeks, I continued improving the posterior parameters dataclasses, enhanced type safety, and resolved type-related issues across trainer classes in sbi.

Enhancing Posterior Parameter Dataclasses

I started by updating the docstrings for all posterior parameter dataclasses to provide clear field-level information. I also added a missing parameter to the MNLE class for passing importance sampling arguments and updated the type annotation for VectorFieldBasedPotential.

Next, I modularized the posterior parameter resolution method from the previous week. This involved:

• Separating the logic for detecting duplicate dictionary parameters and dataclasses.
• Adding checks for consistency of parameter values across multiple sources.
• Structuring the logic into multiple functions to improve readability and maintainability.

I then introduced a PosteriorParameters abstract base class to serve as a parent for all posterior dataclasses. This included:

• Validation of all fields in each dataclass.
• Implementing a __post_init__ method for type conversions of primitive Python types.
• Checking that values passed to fields with Literal annotations are valid.
• Adding a method for creating copies of dataclasses while modifying select fields.

Unit Testing

I implemented comprehensive unit tests to ensure correctness, including:

• Checking that errors are raised for conflicting arguments.
• Verifying that copies of dataclasses maintain unchanged fields correctly.
• Validating that only PosteriorParameters instances are accepted.
• Ensuring proper type conversions for primitive types.
• Testing that string values for Literal fields match the allowed values.

Resolving Return Type Mismatch

I addressed a type-related issue in the append_simulations method raised in Issue #1453. The method had a conflicting return type between the base class NeuralInference and its subclasses (NLE, NPE, VectorFieldInference).

By using Python’s Self type annotation, I fixed this mismatch and opened PR #1622. This change ensures that Pyright correctly infers the return type of subclass calls to append_simulations, resolving the previous type errors.

Test Refactoring and Validation

In Week 8, I refactored tests by moving a commonly used method for obtaining a trained inference into a test fixture, improving test reusability and readability.

I also updated the build_posterior method for the NPE trainer to validate that a prior is explicitly passed when using rejection sampling. This is required to ensure proper posterior sampling behavior. Unit tests were added to confirm that the validation works as intended and raises clear error messages when necessary.

These two weeks focused on improving type safety, parameter validation, and maintainability, while also enhancing the testing framework and fixing a critical return type mismatch in trainer classes.