Method _compute

metrics/seqeval/seqeval.py:120–164

(
        self,
        predictions,
        references,
        suffix: bool = False,
        scheme: Optional[str] = None,
        mode: Optional[str] = None,
        sample_weight: Optional[List[int]] = None,
        zero_division: Union[str, int] = "warn",
    )

        )

    def _compute(
        self,
        predictions,
        references,
        suffix: bool = False,
        scheme: Optional[str] = None,
        mode: Optional[str] = None,
        sample_weight: Optional[List[int]] = None,
        zero_division: Union[str, int] = "warn",
    ):
        if scheme is not None:
            try:
                scheme_module = importlib.import_module("seqeval.scheme")
                scheme = getattr(scheme_module, scheme)
            except AttributeError:
                raise ValueError(f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}")
        report = classification_report(
            y_true=references,
            y_pred=predictions,
            suffix=suffix,
            output_dict=True,
            scheme=scheme,
            mode=mode,
            sample_weight=sample_weight,
            zero_division=zero_division,
        )
        report.pop("macro avg")
        report.pop("weighted avg")
        overall_score = report.pop("micro avg")

        scores = {
            type_name: {
                "precision": score["precision"],
                "recall": score["recall"],
                "f1": score["f1-score"],
                "number": score["support"],
            }
            for type_name, score in report.items()
        }
        scores["overall_precision"] = overall_score["precision"]
        scores["overall_recall"] = overall_score["recall"]
        scores["overall_f1"] = overall_score["f1-score"]
        scores["overall_accuracy"] = accuracy_score(y_true=references, y_pred=predictions)

        return scores