def to_data_frame()

in src/epam/auto_llm_eval/evaluator.py [0:0]

• 16 lines of code
• 2 McCabe index (conditional complexity)

    def to_data_frame(self, metric_name: str) -> dict[str, any]:
        """
        Store the evaluation result as a dataframe row.

        Converts the evaluation result into a dictionary format suitable for
        creating a dataframe row.

        Args:
            metric_name (str): The name of the metric being evaluated.

        Returns:
            dict[str, any]: A dictionary containing evaluation results and
                metadata.
        """
        result_dict: dict[str, any] = {
            "metric": metric_name,
            "score": self.get_score(),
            "weighted_score": self.get_weighted_score(),
            "is_confident": self.is_confident(),
        }

        prob_dict: dict[str, float] = {
            f"score_{grade}": prob for grade, prob in self.probabilities
        }

        merged_dict: dict[str, any] = {
            **result_dict,
            **prob_dict,
            **self.metadata
        }

        return merged_dict