def rbo(original_model: ComplEx, kelpie_model: KelpieComplEx,
original_samples: numpy.array, kelpie_samples: numpy.array):
_, original_ranks, original_predictions = original_model.predict_samples(
original_samples)
_, kelpie_ranks, kelpie_predictions = kelpie_model.predict_samples(
samples=kelpie_samples, original_mode=False)
all_original_ranks = []
for (a, b) in original_ranks:
all_original_ranks.append(a)
all_original_ranks.append(b)
all_kelpie_ranks = []
for (a, b) in kelpie_ranks:
all_kelpie_ranks.append(a)
all_kelpie_ranks.append(b)
original_mrr = mrr(all_original_ranks)
kelpie_mrr = mrr(all_kelpie_ranks)
original_h1 = hits_k(all_original_ranks, 1)
kelpie_h1 = hits_k(all_kelpie_ranks, 1)
rbos = []
for i in range(len(original_samples)):
_original_sample = original_samples[i]
_kelpie_sample = kelpie_samples[i]
original_target_head, _, original_target_tail = _original_sample
kelpie_target_head, _, kelpie_target_tail = _kelpie_sample
original_target_head_index, original_target_tail_index = int(
original_ranks[i][0] - 1), int(original_ranks[i][1] - 1)
kelpie_target_head_index, kelpie_target_tail_index = int(
kelpie_ranks[i][0] - 1), int(kelpie_ranks[i][1] - 1)
# get head and tail predictions
original_head_predictions = original_predictions[i][0]
kelpie_head_predictions = kelpie_predictions[i][0]
original_tail_predictions = original_predictions[i][1]
kelpie_tail_predictions = kelpie_predictions[i][1]
assert original_head_predictions[
original_target_head_index] == original_target_head
assert kelpie_head_predictions[
kelpie_target_head_index] == kelpie_target_head
assert original_tail_predictions[
original_target_tail_index] == original_target_tail
assert kelpie_tail_predictions[
kelpie_target_tail_index] == kelpie_target_tail
# replace the target head id with the same value (-1 in this case)
original_head_predictions[original_target_head_index] = -1
kelpie_head_predictions[kelpie_target_head_index] = -1
# cut both lists at the max rank that the target head obtained, between original and kelpie model
original_head_predictions = original_head_predictions[:
original_target_head_index
+ 1]
kelpie_head_predictions = kelpie_head_predictions[:
kelpie_target_head_index
+ 1]
# replace the target tail id with the same value (-1 in this case)
original_tail_predictions[original_target_tail_index] = -1
kelpie_tail_predictions[kelpie_target_tail_index] = -1
# cut both lists at the max rank that the target tail obtained, between original and kelpie model
original_tail_predictions = original_tail_predictions[:
original_target_tail_index
+ 1]
kelpie_tail_predictions = kelpie_tail_predictions[:
kelpie_target_tail_index
+ 1]
rbos.append(
ranking_similarity.rank_biased_overlap(original_head_predictions,
kelpie_head_predictions))
rbos.append(
ranking_similarity.rank_biased_overlap(original_tail_predictions,
kelpie_tail_predictions))
avg_rbo = float(sum(rbos)) / float(len(rbos))
return avg_rbo, original_mrr, kelpie_mrr, original_h1, kelpie_h1
original_train_samples = kelpie_dataset.original_train_samples
original_valid_samples = kelpie_dataset.original_valid_samples
original_test_samples = kelpie_dataset.original_test_samples
kelpie_train_samples = kelpie_dataset.kelpie_train_samples
kelpie_valid_samples = kelpie_dataset.kelpie_valid_samples
kelpie_test_samples = kelpie_dataset.kelpie_test_samples
original_direct_samples = numpy.vstack(
(original_train_samples, original_valid_samples,
original_test_samples))
kelpie_direct_samples = numpy.vstack(
(kelpie_train_samples, kelpie_valid_samples, kelpie_test_samples))
_, original_ranks, _ = original_model.predict_samples(
samples=original_direct_samples)
_, kelpie_ranks, _ = kelpie_model.predict_samples(
samples=kelpie_direct_samples, original_mode=False)
train_ranks = []
valid_ranks = []
test_ranks = []
for i in range(len(original_train_samples)):
(head_id, relation_id, tail_id) = original_train_samples[i]
cur_original_triple = (head_id, relation_id, tail_id)
(head_id, relation_id, tail_id) = kelpie_train_samples[i]
cur_kelpie_triple = (head_id, relation_id, tail_id)
original_head_rank, original_tail_rank = original_ranks[i]
kelpie_head_rank, kelpie_tail_rank = kelpie_ranks[i]
train_ranks.append((original_head_rank, kelpie_head_rank))
scores, ranks, _ = kelpie_model.predict_sample(sample=kelpie_sample, original_mode=False)
print("\nKelpie model on original test fact: <%s, %s, %s>" % kelpie_sample_tuple)
print("\tDirect fact score: %f; Inverse fact score: %f" % (scores[0], scores[1]))
print("\tHead Rank: %f" % ranks[0])
print("\tTail Rank: %f" % ranks[1])
# results on all facts containing the kelpie entity
print("\nKelpie model on all test facts containing the Kelpie entity:")
mrr, h1 = KelpieEvaluator(kelpie_model).eval(samples=kelpie_test_samples, original_mode=False)
print("\tMRR: %f\n\tH@1: %f" % (mrr, h1))
print("\n\nComputing RBO between original model predictions and Kelpie model predictions...")
rbos = []
_, original_ranks, original_predictions = original_model.predict_samples(original_entity_test_samples)
_, kelpie_ranks, kelpie_predictions = kelpie_model.predict_samples(samples=kelpie_test_samples, original_mode=False)
for i in range(len(original_entity_test_samples)):
original_sample = original_entity_test_samples[i]
kelpie_sample = kelpie_test_samples[i]
original_target_head, _, original_target_tail = original_sample
kelpie_target_head, _, kelpie_target_tail = kelpie_sample
original_target_head_index, original_target_tail_index = int(original_ranks[i][0]-1), int(original_ranks[i][1]-1)
kelpie_target_head_index, kelpie_target_tail_index= int(kelpie_ranks[i][0]-1), int(kelpie_ranks[i][1]-1)
# get head and tail predictions
original_head_predictions = original_predictions[i][0]
