def find_tp_ranks()

in isc/metrics.py [0:0]

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

def find_tp_ranks(gt_matches: List[GroundTruthMatch], predictions: List[PredictedMatch]):
    q_to_res = defaultdict(list)
    for p in predictions:
        q_to_res[p.query].append(p)
    ranks = []
    not_found = int(1<<35)
    for m in gt_matches:
        if m.query not in q_to_res:
            ranks.append(not_found)
            continue
        res = q_to_res[m.query]
        res = np.array([
            (p.score, m.db == p.db)
            for p in res
        ])
        i, = np.where(res[:, 1] == 1)
        if i.size == 0:
            ranks.append(not_found)
        else:
            i = i[0]
            rank = (res[:, 0] >= res[i, 0]).sum() - 1
            ranks.append(rank)
    return np.array(ranks)