def __init__()

in src/fmeval/reporting/eval_output_cells.py [0:0]

• 34 lines of code
• 6 McCabe index (conditional complexity)

    def __init__(self, categories: List[str], scores: List[float], score_name: str, dataset_score: float, n: int = 10):
        """
        :param categories: The names of the categories.
        :param scores: The values of the category scores.
        :param score_name: The name of the score that was computed in the evaluation.
        :param dataset_score: The overall score for the dataset.
        :param n: Max number of categories to display.
        """

        note = (
            f"The top {n} categories are displayed here. To view the remaining category scores, see the `output.json` file at your S3 output location."
            if len(categories) > n
            else ""
        )
        sorted_scores, sorted_categories = (list(l) for l in zip(*sorted(zip(scores, categories), reverse=True)))
        bar_plot_origin = 0.5 if score_name == PROMPT_STEREOTYPING else 0
        bar_plot = CategoryBarPlotCell(
            sorted_categories[:n],
            sorted_scores[:n],
            score_name,
            dataset_score,
            height="70%",
            width="70%",
            origin=bar_plot_origin,
        )

        lowest_category = (
            CategoryScoreCell._get_kth_category_score(categories, scores, reverse=True, origin=0.5, k=0)
            if score_name == PROMPT_STEREOTYPING
            else CategoryScoreCell._get_kth_category_score(categories, scores, k=0)
        )
        lowest_category = escape(lowest_category)
        lowest_score_description = (
            "The model stereotypes the most in the category"
            if score_name == PROMPT_STEREOTYPING
            else "The model scores lowest in the category"
        )
        super().__init__(
            f"The plot shows the score breakdown into individual categories.",
            note,
            bar_plot,
            f"{lowest_score_description} **{lowest_category}**. ",
        )