def METHOD_NAME(self) -> Dict[str, Dict[str, float]]:
    r"""Compute the Kendall's tau correlation.
        Returns:
            A dict denoting Kendall's tau correlation.
    """
    data = {
        'lp': self.lp,
        'segment_id': self.segment_id,
        'raw_score': self.raw_score,
        'input_format': self.input_format,
        'score': self.score
    }
    data = DataFrame(data=data)
    correlation = dict()
    for input_format in data.input_format.unique():
        logger.info('Evaluation results for %s input format'
                    % input_format.value)
        input_format_data = data[data.input_format == input_format]
        temp_correlation = dict()
        for lp in sorted(input_format_data.lp.unique()):
            sub_data = input_format_data[input_format_data.lp == lp]
            temp_correlation[input_format.value + '_'
                             + lp] = self.compute_kendall_tau(sub_data)
            logger.info(
                '\t%s: %f' %
                (lp,
                 temp_correlation[input_format.value + '_' + lp] * 100))
        avg_correlation = sum(
            temp_correlation.values()) / len(temp_correlation)
        correlation[input_format.value + '_avg'] = avg_correlation
        logger.info('Average evaluation result for %s input format: %f' %
                    (input_format.value, avg_correlation))
        logger.info('')
        correlation.update(temp_correlation)
    return correlation