def eval(
self,
scores: Scores,
batch_edges: EdgeList,
) -> Stats:
batch_size = len(batch_edges)
ranks = []
aucs = []
if scores.lhs_neg.nelement() > 0:
lhs_rank = (scores.lhs_neg >=
scores.lhs_pos.unsqueeze(1)).sum(1) + 1
lhs_auc = compute_randomized_auc(scores.lhs_pos, scores.lhs_neg,
batch_size)
ranks.append(lhs_rank)
aucs.append(lhs_auc)
if scores.rhs_neg.nelement() > 0:
rhs_rank = (scores.rhs_neg >=
scores.rhs_pos.unsqueeze(1)).sum(1) + 1
rhs_auc = compute_randomized_auc(scores.rhs_pos, scores.rhs_neg,
batch_size)
ranks.append(rhs_rank)
aucs.append(rhs_auc)
return Stats(
pos_rank=average_of_sums(*ranks),
mrr=average_of_sums(*(rank.float().reciprocal()
for rank in ranks)),
r1=average_of_sums(*(rank.le(1) for rank in ranks)),
r10=average_of_sums(*(rank.le(10) for rank in ranks)),
r50=average_of_sums(*(rank.le(50) for rank in ranks)),
# At the end the AUC will be averaged over count.
auc=batch_size * sum(aucs) / len(aucs),
count=batch_size)
def eval(
self,
scores: Scores,
batch_edges: EdgeList,
) -> Stats:
batch_size = len(batch_edges)
lhs_rank = (scores.lhs_neg >= scores.lhs_pos.unsqueeze(1)).sum(1) + 1
rhs_rank = (scores.rhs_neg >= scores.rhs_pos.unsqueeze(1)).sum(1) + 1
lhs_auc = compute_randomized_auc(scores.lhs_pos, scores.lhs_neg,
batch_size)
rhs_auc = compute_randomized_auc(scores.rhs_pos, scores.rhs_neg,
batch_size)
return Stats(
pos_rank=average_of_sums(lhs_rank, rhs_rank),
mrr=average_of_sums(lhs_rank.float().reciprocal(),
rhs_rank.float().reciprocal()),
r1=average_of_sums(lhs_rank.le(1), rhs_rank.le(1)),
r10=average_of_sums(lhs_rank.le(10), rhs_rank.le(10)),
r50=average_of_sums(lhs_rank.le(50), rhs_rank.le(50)),
# At the end the AUC will be averaged over count.
auc=batch_size * (lhs_auc + rhs_auc) / 2,
count=batch_size)
def _process_one_batch(self, model: MultiRelationEmbedder,
batch_edges: EdgeList) -> Stats:
with torch.no_grad():
scores = model(batch_edges)
self._adjust_scores(scores, batch_edges)
batch_size = len(batch_edges)
loss = self.calc_loss(scores, batch_edges)
ranks = []
aucs = []
if scores.lhs_neg.nelement() > 0:
lhs_rank = (scores.lhs_neg >=
scores.lhs_pos.unsqueeze(1)).sum(1) + 1
lhs_auc = compute_randomized_auc(scores.lhs_pos, scores.lhs_neg,
batch_size)
ranks.append(lhs_rank)
aucs.append(lhs_auc)
if scores.rhs_neg.nelement() > 0:
rhs_rank = (scores.rhs_neg >=
scores.rhs_pos.unsqueeze(1)).sum(1) + 1
rhs_auc = compute_randomized_auc(scores.rhs_pos, scores.rhs_neg,
batch_size)
ranks.append(rhs_rank)
aucs.append(rhs_auc)
return Stats(
loss=float(loss),
pos_rank=average_of_sums(*ranks),
mrr=average_of_sums(*(rank.float().reciprocal()
for rank in ranks)),
r1=average_of_sums(*(rank.le(1) for rank in ranks)),
r10=average_of_sums(*(rank.le(10) for rank in ranks)),
r50=average_of_sums(*(rank.le(50) for rank in ranks)),
# At the end the AUC will be averaged over count.
auc=batch_size * sum(aucs) / len(aucs),
count=batch_size,
)
