def calc_raw_mrr()

in source/sagemaker/sagemaker_graph_entity_resolution/dgl_entity_resolution/utils.py [0:0]

• 16 lines of code
• 2 McCabe index (conditional complexity)

def calc_raw_mrr(embedding, w, test_triplets, hits=[], eval_bz=100):
    with torch.no_grad():
        s = test_triplets[:, 0]
        r = test_triplets[:, 1]
        o = test_triplets[:, 2]
        test_size = test_triplets.shape[0]

        # perturb subject
        ranks_s = perturb_and_get_raw_rank(embedding, w, o, r, s, test_size, eval_bz)
        # perturb object
        ranks_o = perturb_and_get_raw_rank(embedding, w, s, r, o, test_size, eval_bz)

        ranks = torch.cat([ranks_s, ranks_o])
        ranks += 1 # change to 1-indexed

        mrr = torch.mean(1.0 / ranks.float())
        print("MRR (raw): {:.6f}".format(mrr.item()))

        for hit in hits:
            avg_count = torch.mean((ranks <= hit).float())
            print("Hits (raw) @ {}: {:.6f}".format(hit, avg_count.item()))
    return mrr.item()