def run_kelpie(train_samples):
print("Wrapping the original model in a Kelpie explainable model...")
# use model_to_explain to initialize the Kelpie model
kelpie_model = KelpieComplEx(model=original_model, dataset=kelpie_dataset, init_size=1e-3) # type: KelpieComplEx
kelpie_model.to('cuda')
########### BUILD THE OPTIMIZER AND RUN POST-TRAINING
print("Running post-training on the Kelpie model...")
optimizer = KelpieMultiClassNLLptimizer(model=kelpie_model,
optimizer_name=args.optimizer,
batch_size=args.batch_size,
learning_rate=args.learning_rate,
decay1=args.decay1,
decay2=args.decay2,
regularizer_name="N3",
regularizer_weight=args.reg)
optimizer.train(train_samples=train_samples, max_epochs=args.max_epochs)
########### EXTRACT RESULTS
print("\nExtracting results...")
kelpie_entity_id = kelpie_dataset.kelpie_entity_id
kelpie_sample_tuple = (kelpie_entity_id, relation_id, tail_id) if args.perspective == "head" else (head_id, relation_id, kelpie_entity_id)
kelpie_sample = numpy.array(kelpie_sample_tuple)
### Evaluation on original entity
# Kelpie model on original fact
scores, ranks, predictions = kelpie_model.predict_sample(sample=original_sample, original_mode=True)
original_direct_score, original_inverse_score = scores[0], scores[1]
original_head_rank, original_tail_rank = ranks[0], ranks[1]
print("\nKelpie model on the original test fact: <%s, %s, %s>" % original_triple)
print("\tDirect fact score: %f; Inverse fact score: %f" % (original_direct_score, original_inverse_score))
print("\tHead Rank: %f" % original_head_rank)
print("\tTail Rank: %f" % original_tail_rank)
# Kelpie model on all facts containing the original entity
print("\nKelpie model on all test facts containing the original entity:")
mrr, h1 = KelpieEvaluator(kelpie_model).eval(samples=original_test_samples, original_mode=True)
print("\tMRR: %f\n\tH@1: %f" % (mrr, h1))
### Evaluation on kelpie entity
# results on kelpie fact
scores, ranks, _ = kelpie_model.predict_sample(sample=kelpie_sample, original_mode=False)
kelpie_direct_score, kelpie_inverse_score = scores[0], scores[1]
kelpie_head_rank, kelpie_tail_rank = ranks[0], ranks[1]
print("\nKelpie model on the Kelpie test fact: <%s, %s, %s>" % kelpie_sample_tuple)
print("\tDirect fact score: %f; Inverse fact score: %f" % (kelpie_direct_score, kelpie_inverse_score))
print("\tHead Rank: %f" % kelpie_head_rank)
print("\tTail Rank: %f" % kelpie_tail_rank)
# 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))
return kelpie_direct_score, kelpie_inverse_score, kelpie_head_rank, kelpie_tail_rank
assert (original_sample in complex_dataset.test_samples)
kelpie_dataset = KelpieDataset(dataset=complex_dataset,
entity_id=original_entity_id)
kelpie_entity_id = kelpie_dataset.kelpie_entity_id
kelpie_triple = (kelpie_entity_id, relation_id,
tail_id) if perspective == 'head' else (head_id,
relation_id,
kelpie_entity_id)
kelpie_sample = numpy.array(kelpie_triple)
print("Wrapping the original model in a Kelpie model...")
kelpie_model = KelpieComplEx(dataset=kelpie_dataset,
model=original_model,
init_size=1e-3) # type: KelpieComplEx
kelpie_model.to('cuda')
print("Running post-training on the Kelpie model...")
optimizer = KelpieMultiClassNLLptimizer(
model=kelpie_model,
optimizer_name=optimizer_name,
batch_size=batch_size,
learning_rate=learning_rate,
decay1=decay1,
decay2=decay2,
regularizer_name=regularizer_name,
regularizer_weight=regularizer_weight)
optimizer.train(train_samples=kelpie_dataset.kelpie_train_samples,
max_epochs=max_epochs)