def get_negation_metric()

in lama/evaluation_metrics.py [0:0]

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

def get_negation_metric(log_probs, masked_indices, log_probs_negated,
                        masked_indices_negated, vocab, index_list=None,
                        topk = 1):

    return_msg = ""
    # if negated sentence present
    if len(masked_indices_negated) > 0:

        log_probs, index_max_probs, _ = \
            __max_probs_values_indices(masked_indices, log_probs, topk=topk)
        log_probs_negated, index_max_probs_negated, _ = \
            __max_probs_values_indices(masked_indices_negated,
                                       log_probs_negated, topk=topk)

        # overlap btw. affirmative and negated first ranked prediction: 0 or 1
        overlap = __overlap_negation(index_max_probs_negated[0],
                                     index_max_probs[0])
        # rank corrl. btw. affirmative and negated predicted log_probs
        spearman_rank_corr = scipy.stats.spearmanr(log_probs,
                                                   log_probs_negated)[0]

    else:
        overlap = np.nan
        spearman_rank_corr = np.nan

    return overlap, spearman_rank_corr, return_msg