PR Summary

This pull request enhances and cleans up the codebase by performing several refactoring tasks and improvements to type annotations and documentation. The key changes include:

1. In the LLMAgentDataset class:

   • Improved type annotations, explicitly importing and using types like Callable, Any, and Optional[str].
   • Enhanced docstrings for methods such as _convert_to_dataframe, _serialize_list_field, _serialize_tools_field, _deserialize_list_field, and various class methods with clearer Args and Returns sections.
   • Minor adjustments in method signatures to ensure proper typing, such as replacing ambiguous list types with List[Any] and moving the **kwargs typing to Any.

2. In various scoring functions under the classification package (e.g., AbsoluteError, BrierScore, CalibrationError, ClassBalance, Confidence, Correctness, LogLoss, OutlierScore, ProbabilityError, Uncertainty):

   • Removed the unused **kwargs parameters to simplify the function signatures as they were only provided for backwards compatibility and not used.

3. Minor modifications in the logging function in the result module by cleaning up parameter documentation.

Overall, the changes focus on improving code clarity, consistency in type hints and documentation, and reducing unnecessary parameters, which will aid in maintenance and future development.

Test Suggestions

• Write unit tests for LLMAgentDataset to verify that data conversion, serialization, and deserialization functions work as intended.
• Create tests to validate that adding test cases and goldens correctly updates the underlying pandas DataFrame.
• Test the conversion methods (from_test_cases, from_goldens, from_deepeval_dataset) to ensure they correctly instantiate the dataset.
• Run regression tests for all classification scoring functions to ensure their logic remains unchanged after the removal of **kwargs.
• Include integration tests to verify that the dataset evaluation with DeepEval metrics produces expected results.