def lea()

in modeling/coval/eval/evaluator.py [0:0]

• 22 lines of code
• 12 McCabe index (conditional complexity)

def lea(input_clusters, output_clusters, mention_to_gold):
    num, den = 0, 0

    for c in input_clusters:
        if len(c) == 1:
            all_links = 1
            if c[0] in mention_to_gold and len(
                    output_clusters[mention_to_gold[c[0]]]) == 1:
                common_links = 1
            else:
                common_links = 0
        else:
            common_links = 0
            all_links = len(c) * (len(c) - 1) / 2.0
            for i, m in enumerate(c):
                if m in mention_to_gold:
                    for m2 in c[i + 1:]:
                        if m2 in mention_to_gold and mention_to_gold[
                                m] == mention_to_gold[m2]:
                            common_links += 1
                        # else:
                        #    print('!! ', m2, '--', m2.get_span(), ' ',
                        #           m2.min_spans, ' ', mention_to_gold[m], ' ',
                        #           mention_to_gold[m2], ' ' ,
                        #           [str(s) for s in output_clusters[
                        #               mention_to_gold[m]]], ' -- ',
                        #           [str(s) for s in output_clusters[
                        #               mention_to_gold[m2]]])

        num += len(c) * common_links / float(all_links)
        den += len(c)

    return num, den