def evaluate_sample()

in src/fmeval/eval_algorithms/classification_accuracy.py [0:0]

• 18 lines of code
• 1 McCabe index (conditional complexity)

    def evaluate_sample(self, target_output: str, model_output: str) -> List[EvalScore]:
        """Compute classification accuracy metrics for a single sample.

        :param target_output: The expected/desired model output.
        :param model_output: The actual model output.
        :returns: A single-element list with an EvalScore for the classification accuracy score.
        """
        util.require(
            self.valid_labels,
            "ClassificationAccuracy evaluate_sample method requires the `valid_labels` "
            "attribute of the ClassificationAccuracy instance to be set.",
        )
        sample = {
            DatasetColumns.TARGET_OUTPUT.value.name: target_output,
            DatasetColumns.MODEL_OUTPUT.value.name: model_output,
        }
        pipeline = self._build_pipeline(self.valid_labels)
        result = pipeline.execute_record(sample)
        return [
            EvalScore(
                name=CLASSIFICATION_ACCURACY_SCORE,
                value=result[CLASSIFICATION_ACCURACY_SCORE],  # type: ignore
            )
        ]